| 1 : |
agomez |
1 |
/* ABSTRACT: */
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/* Simulated annealing is a global optimization method that distinguishes */
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/* different local optima. Starting from an initial point, the algorithm */
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/* takes a step and the function is evaluated. When minimizing a function,*/
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/* any downhill step is accepted and the process repeats from this new */
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/* point. An uphill step may be accepted (thus, it can escape from local */
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/* optima). This uphill decision is made by the Metropolis criteria. As */
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/* the optimization process proceeds, the length of the steps decline and */
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/* the algorithm closes in on the global optimum. Since the algorithm */
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/* makes very few assumptions regarding the function to be optimized, it */
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/* is quite robust with respect to non-quadratic surfaces. The degree of */
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/* robustness can be adjusted by the user. In fact, simulated annealing */
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/* can be used as a local optimizer for difficult functions. */
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/* */
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/* The author can be contacted at h2zr1001@vm.cis.smu.edu */
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ulcessvp |
11 |
//
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agomez |
1 |
/* This file is a translation of a fortran code, which is an example of the*/
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/* Corana et al. simulated annealing algorithm for multimodal and robust */
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/* optimization as implemented and modified by by Goffe et al. */
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/* */
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/* Use the sample function from Judge with the following suggestions */
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/* to get a feel for how SA works. When you've done this, you should be */
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/* ready to use it on most any function with a fair amount of expertise. */
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/* 1. Run the program as is to make sure it runs okay. Take a look at */
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/* the intermediate output and see how it optimizes as temperature */
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/* (T) falls. Notice how the optimal point is reached and how */
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/* falling T reduces VM. */
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/* 2. Look through the documentation to SA so the following makes a */
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/* bit of sense. In line with the paper, it shouldn't be that hard */
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/* to figure out. The core of the algorithm is described on pp. 4-6 */
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/* and on pp. 28. Also see Corana et al. pp. 264-9. */
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/* 3. To see the importance of different temperatures, try starting */
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/* with a very low one (say T = 10E-5). You'll see (i) it never */
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/* escapes from the local optima (in annealing terminology, it */
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/* quenches) & (ii) the step length (VM) will be quite small. This */
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/* is a key part of the algorithm: as temperature (T) falls, step */
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/* length does too. In a minor point here, note how VM is quickly */
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/* reset from its initial value. Thus, the input VM is not very */
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/* important. This is all the more reason to examine VM once the */
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/* algorithm is underway. */
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/* 4. To see the effect of different parameters and their effect on */
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/* the speed of the algorithm, try RT = .95 & RT = .1. Notice the */
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/* vastly different speed for optimization. Also try NT = 20. Note */
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/* that this sample function is quite easy to optimize, so it will */
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/* tolerate big changes in these parameters. RT and NT are the */
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/* parameters one should adjust to modify the runtime of the */
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/* algorithm and its robustness. */
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/* 5. Try constraining the algorithm with either LB or UB. */
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ulcessvp |
11 |
//
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agomez |
1 |
/* Synopsis: */
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/* This routine implements the continuous simulated annealing global */
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/* optimization algorithm described in Corana et al.'s article */
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/* "Minimizing Multimodal Functions of Continuous Variables with the */
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/* "Simulated Annealing" Algorithm" in the September 1987 (vol. 13, */
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/* no. 3, pp. 262-280) issue of the ACM Transactions on Mathematical */
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/* Software. */
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ulcessvp |
11 |
//
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| 58 : |
agomez |
1 |
/* A very quick (perhaps too quick) overview of SA: */
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/* SA tries to find the global optimum of an N dimensional function. */
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/* It moves both up and downhill and as the optimization process */
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/* proceeds, it focuses on the most promising area. */
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/* To start, it randomly chooses a trial point within the step length */
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/* VM (a vector of length N) of the user selected starting point. The */
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/* function is evaluated at this trial point and its value is compared */
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/* to its value at the initial point. */
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/* In a maximization problem, all uphill moves are accepted and the */
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/* algorithm continues from that trial point. Downhill moves may be */
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/* accepted; the decision is made by the Metropolis criteria. It uses T */
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/* (temperature) and the size of the downhill move in a probabilistic */
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/* manner. The smaller T and the size of the downhill move are, the more */
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/* likely that move will be accepted. If the trial is accepted, the */
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/* algorithm moves on from that point. If it is rejected, another point */
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/* is chosen instead for a trial evaluation. */
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/* Each element of VM periodically adjusted so that half of all */
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/* function evaluations in that direction are accepted. */
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/* A fall in T is imposed upon the system with the RT variable by */
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/* T(i+1) = RT*T(i) where i is the ith iteration. Thus, as T declines, */
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/* downhill moves are less likely to be accepted and the percentage of */
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/* rejections rise. Given the scheme for the selection for VM, VM falls. */
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/* Thus, as T declines, VM falls and SA focuses upon the most promising */
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/* area for optimization. */
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ulcessvp |
11 |
//
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agomez |
1 |
/* The importance of the parameter T: */
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/* The parameter T is crucial in using SA successfully. It influences */
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/* VM, the step length over which the algorithm searches for optima. For */
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/* a small intial T, the step length may be too small; thus not enough */
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/* of the function might be evaluated to find the global optima. The user */
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/* should carefully examine VM in the intermediate output (set IPRINT = */
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/* 1) to make sure that VM is appropriate. The relationship between the */
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/* initial temperature and the resulting step length is function */
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/* dependent. */
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/* To determine the starting temperature that is consistent with */
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/* optimizing a function, it is worthwhile to run a trial run first. Set */
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/* RT = 1.5 and T = 1.0. With RT > 1.0, the temperature increases and VM */
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/* rises as well. Then select the T that produces a large enough VM. */
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ulcessvp |
11 |
//
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agomez |
1 |
/* For modifications to the algorithm and many details on its use, */
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/* (particularly for econometric applications) see Goffe, Ferrier */
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/* and Rogers, "Global Optimization of Statistical Functions with */
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/* the Simulated Annealing," Journal of Econometrics (forthcoming) */
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/* For a pre-publication copy, contact */
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/* Bill Goffe */
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/* Department of Economics */
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/* Southern Methodist University */
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/* Dallas, TX 75275 */
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/* h2zr1001 @ smuvm1 (Bitnet) */
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/* h2zr1001 @ vm.cis.smu.edu (Internet) */
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ulcessvp |
11 |
//
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agomez |
1 |
/* As far as possible, the parameters here have the same name as in */
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/* the description of the algorithm on pp. 266-8 of Corana et al. */
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ulcessvp |
11 |
//
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agomez |
1 |
/* Input Parameters: */
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/* Note: The suggested values generally come from Corana et al. To */
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/* drastically reduce runtime, see Goffe et al., pp. 17-8 for */
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/* suggestions on choosing the appropriate RT and NT. */
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/* n - Number of variables in the function to be optimized. (INT) */
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/* x - The starting values for the variables of the function to be */
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/* optimized. (DP(N)) */
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/* max - Denotes whether the function should be maximized or */
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/* minimized. A true value denotes maximization while a false */
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/* value denotes minimization. */
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/* RT - The temperature reduction factor. The value suggested by */
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/* Corana et al. is .85. See Goffe et al. for more advice. (DP) */
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/* EPS - Error tolerance for termination. If the final function */
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/* values from the last neps temperatures differ from the */
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/* corresponding value at the current temperature by less than */
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/* EPS and the final function value at the current temperature */
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/* differs from the current optimal function value by less than */
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/* EPS, execution terminates and IER = 0 is returned. (EP) */
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/* NS - Number of cycles. After NS*N function evaluations, each element */
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/* of VM is adjusted so that approximately half of all function */
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/* evaluations are accepted. The suggested value is 20. (INT) */
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/* nt - Number of iterations before temperature reduction. After */
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/* NT*NS*N function evaluations, temperature (T) is changed */
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/* by the factor RT. Value suggested by Corana et al. is */
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/* MAX(100, 5*N). See Goffe et al. for further advice. (INT) */
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/* NEPS - Number of final function values used to decide upon termi- */
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/* nation. See EPS. Suggested value is 4. (INT) */
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/* maxevl - The maximum number of function evaluations. If it is */
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/* exceeded, IER = 1. (INT) */
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/* lb - The lower bound for the allowable solution variables. (DP(N)) */
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/* ub - The upper bound for the allowable solution variables. (DP(N)) */
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/* If the algorithm chooses X(I) .LT. LB(I) or X(I) .GT. UB(I), */
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/* I = 1, N, a point is from inside is randomly selected. This */
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/* This focuses the algorithm on the region inside UB and LB. */
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/* Unless the user wishes to concentrate the search to a par- */
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/* ticular region, UB and LB should be set to very large positive */
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/* and negative values, respectively. Note that the starting */
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/* vector X should be inside this region. Also note that LB and */
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/* UB are fixed in position, while VM is centered on the last */
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/* accepted trial set of variables that optimizes the function. */
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/* c - Vector that controls the step length adjustment. The suggested */
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/* value for all elements is 2.0. (DP(N)) */
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/* t - On input, the initial temperature. See Goffe et al. for advice. */
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/* On output, the final temperature. (DP) */
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/* vm - The step length vector. On input it should encompass the */
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/* region of interest given the starting value X. For point */
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/* X(I), the next trial point is selected is from X(I) - VM(I) */
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/* to X(I) + VM(I). Since VM is adjusted so that about half */
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/* of all points are accepted, the input value is not very */
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/* important (i.e. is the value is off, SA adjusts VM to the */
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/* correct value). (DP(N)) */
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ulcessvp |
11 |
//
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agomez |
1 |
/* Output Parameters: */
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/* xopt - The variables that optimize the function. (DP(N)) */
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/* fopt - The optimal value of the function. (DP) */
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ulcessvp |
11 |
//
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agomez |
1 |
/* JMB this has been modified to work with the gadget object structure */
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/* This means that the function has been replaced by a call to ecosystem */
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/* object, and we can use the vector objects that have been defined */
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#include "gadget.h" |
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#include "optinfo.h" |
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#include "mathfunc.h" |
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#include "doublevector.h" |
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#include "intvector.h" |
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#include "errorhandler.h" |
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#include "ecosystem.h" |
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#include "global.h" |
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ulcessvp |
11 |
#include "seq_optimize_template.h" |
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ulcessvp |
15 |
#ifdef SPECULATIVE
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| 182 : |
ulcessvp |
11 |
#include <omp.h>
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| 183 : |
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#endif
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agomez |
1 |
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extern Ecosystem* EcoSystem;
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ulcessvp |
11 |
#ifndef NO_OPENMP
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| 187 : |
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extern Ecosystem** EcoSystems;
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#endif
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agomez |
1 |
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ulcessvp |
12 |
/*sequential code replaced at seq_optimize_template.h*/
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ulcessvp |
11 |
//void OptInfoSimann::OptimiseLikelihood() {
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| 192 : |
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//
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| 193 : |
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// //set initial values
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| 194 : |
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// int nacc = 0; //The number of accepted function evaluations
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// int nrej = 0; //The number of rejected function evaluations
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// int naccmet = 0; //The number of metropolis accepted function evaluations
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//
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// double tmp, p, pp, ratio, nsdiv;
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// double fopt, funcval, trialf;
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// int a, i, j, k, l, offset, quit;
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// int rchange, rcheck, rnumber; //Used to randomise the order of the parameters
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//
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// handle.logMessage(LOGINFO, "\nStarting Simulated Annealing optimisation algorithm\n");
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// int nvars = EcoSystem->numOptVariables();
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// DoubleVector x(nvars);
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// DoubleVector init(nvars);
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// DoubleVector trialx(nvars, 0.0);
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// DoubleVector bestx(nvars);
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// DoubleVector scalex(nvars);
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| 210 : |
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// DoubleVector lowerb(nvars);
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| 211 : |
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// DoubleVector upperb(nvars);
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| 212 : |
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// DoubleVector fstar(tempcheck);
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| 213 : |
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// DoubleVector vm(nvars, vminit);
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| 214 : |
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// IntVector param(nvars, 0);
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| 215 : |
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// IntVector nacp(nvars, 0);
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| 216 : |
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//
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| 217 : |
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// EcoSystem->resetVariables(); //JMB need to reset variables in case they have been scaled
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// if (scale)
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// EcoSystem->scaleVariables();
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| 220 : |
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// EcoSystem->getOptScaledValues(x);
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// EcoSystem->getOptLowerBounds(lowerb);
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| 222 : |
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// EcoSystem->getOptUpperBounds(upperb);
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| 223 : |
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// EcoSystem->getOptInitialValues(init);
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| 224 : |
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//
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| 225 : |
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// for (i = 0; i < nvars; i++) {
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| 226 : |
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// bestx[i] = x[i];
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| 227 : |
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// param[i] = i;
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| 228 : |
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// }
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| 229 : |
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//
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| 230 : |
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// if (scale) {
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| 231 : |
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// for (i = 0; i < nvars; i++) {
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| 232 : |
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// scalex[i] = x[i];
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| 233 : |
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// // Scaling the bounds, because the parameters are scaled
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| 234 : |
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// lowerb[i] = lowerb[i] / init[i];
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| 235 : |
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// upperb[i] = upperb[i] / init[i];
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| 236 : |
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// if (lowerb[i] > upperb[i]) {
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| 237 : |
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// tmp = lowerb[i];
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| 238 : |
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// lowerb[i] = upperb[i];
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| 239 : |
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// upperb[i] = tmp;
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| 240 : |
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// }
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| 241 : |
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// }
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| 242 : |
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// }
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| 243 : |
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//
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| 244 : |
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// //funcval is the function value at x
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| 245 : |
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// funcval = EcoSystem->SimulateAndUpdate(x);
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| 246 : |
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// if (funcval != funcval) { //check for NaN
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| 247 : |
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// handle.logMessage(LOGINFO, "Error starting Simulated Annealing optimisation with f(x) = infinity");
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| 248 : |
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// converge = -1;
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| 249 : |
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// iters = 1;
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| 250 : |
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// return;
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| 251 : |
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// }
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| 252 : |
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//
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| 253 : |
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// //the function is to be minimised so switch the sign of funcval (and trialf)
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| 254 : |
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// funcval = -funcval;
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| 255 : |
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// offset = EcoSystem->getFuncEval(); //number of function evaluations done before loop
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| 256 : |
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// nacc++;
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| 257 : |
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// cs /= lratio; //JMB save processing time
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| 258 : |
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// nsdiv = 1.0 / ns;
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| 259 : |
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// fopt = funcval;
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| 260 : |
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// for (i = 0; i < tempcheck; i++)
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| 261 : |
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// fstar[i] = funcval;
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| 262 : |
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//
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| 263 : |
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// //Start the main loop. Note that it terminates if
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| 264 : |
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// //(i) the algorithm succesfully optimises the function or
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| 265 : |
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// //(ii) there are too many function evaluations
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| 266 : |
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// while (1) {
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| 267 : |
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// for (a = 0; a < nt; a++) {
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| 268 : |
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// //Randomize the order of the parameters once in a while, to avoid
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| 269 : |
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// //the order having an influence on which changes are accepted
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| 270 : |
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// rchange = 0;
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| 271 : |
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// while (rchange < nvars) {
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| 272 : |
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// rnumber = rand_r(&seedP) % nvars;
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| 273 : |
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// rcheck = 1;
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| 274 : |
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// for (i = 0; i < rchange; i++)
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| 275 : |
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// if (param[i] == rnumber)
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| 276 : |
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// rcheck = 0;
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| 277 : |
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// if (rcheck) {
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| 278 : |
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// param[rchange] = rnumber;
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| 279 : |
|
|
// rchange++;
|
| 280 : |
|
|
// }
|
| 281 : |
|
|
// }
|
| 282 : |
|
|
//
|
| 283 : |
|
|
// for (j = 0; j < ns; j++) {
|
| 284 : |
|
|
// for (l = 0; l < nvars; l++) {
|
| 285 : |
|
|
// //Generate trialx, the trial value of x
|
| 286 : |
|
|
// newValue(nvars, l, param, trialx, x, lowerb, upperb, vm);
|
| 287 : |
|
|
//// for (i = 0; i < nvars; i++) {
|
| 288 : |
|
|
//// if (i == param[l]) {
|
| 289 : |
|
|
//// trialx[i] = x[i] + ((randomNumber() * 2.0) - 1.0) * vm[i];
|
| 290 : |
|
|
////
|
| 291 : |
|
|
//// //If trialx is out of bounds, try again until we find a point that is OK
|
| 292 : |
|
|
//// if ((trialx[i] < lowerb[i]) || (trialx[i] > upperb[i])) {
|
| 293 : |
|
|
//// //JMB - this used to just select a random point between the bounds
|
| 294 : |
|
|
//// k = 0;
|
| 295 : |
|
|
//// while ((trialx[i] < lowerb[i]) || (trialx[i] > upperb[i])) {
|
| 296 : |
|
|
//// trialx[i] = x[i] + ((randomNumber() * 2.0) - 1.0) * vm[i];
|
| 297 : |
|
|
//// k++;
|
| 298 : |
|
|
//// if (k > 10) //we've had 10 tries to find a point neatly, so give up
|
| 299 : |
|
|
//// trialx[i] = lowerb[i] + (upperb[i] - lowerb[i]) * randomNumber();
|
| 300 : |
|
|
//// }
|
| 301 : |
|
|
//// }
|
| 302 : |
|
|
////
|
| 303 : |
|
|
//// } else
|
| 304 : |
|
|
//// trialx[i] = x[i];
|
| 305 : |
|
|
//// }
|
| 306 : |
|
|
//
|
| 307 : |
|
|
// //Evaluate the function with the trial point trialx and return as -trialf
|
| 308 : |
|
|
// trialf = EcoSystem->SimulateAndUpdate(trialx);
|
| 309 : |
|
|
// trialf = -trialf;
|
| 310 : |
|
|
//
|
| 311 : |
|
|
// //If too many function evaluations occur, terminate the algorithm
|
| 312 : |
|
|
// iters = EcoSystem->getFuncEval() - offset;
|
| 313 : |
|
|
// if (iters > simanniter) {
|
| 314 : |
|
|
// handle.logMessage(LOGINFO, "\nStopping Simulated Annealing optimisation algorithm\n");
|
| 315 : |
|
|
// handle.logMessage(LOGINFO, "The optimisation stopped after", iters, "function evaluations");
|
| 316 : |
|
|
// handle.logMessage(LOGINFO, "The temperature was reduced to", t);
|
| 317 : |
|
|
// handle.logMessage(LOGINFO, "The optimisation stopped because the maximum number of function evaluations");
|
| 318 : |
|
|
// handle.logMessage(LOGINFO, "was reached and NOT because an optimum was found for this run");
|
| 319 : |
|
|
// handle.logMessage(LOGINFO, "Number of directly accepted points", nacc);
|
| 320 : |
|
|
// handle.logMessage(LOGINFO, "Number of metropolis accepted points", naccmet);
|
| 321 : |
|
|
// handle.logMessage(LOGINFO, "Number of rejected points", nrej);
|
| 322 : |
|
|
//
|
| 323 : |
|
|
// score = EcoSystem->SimulateAndUpdate(bestx);
|
| 324 : |
|
|
// handle.logMessage(LOGINFO, "\nSimulated Annealing finished with a likelihood score of", score);
|
| 325 : |
|
|
// return;
|
| 326 : |
|
|
// }
|
| 327 : |
|
|
// //Accept the new point if the new function value better
|
| 328 : |
|
|
// if ((trialf - funcval) > verysmall) {
|
| 329 : |
|
|
// for (i = 0; i < nvars; i++)
|
| 330 : |
|
|
// x[i] = trialx[i];
|
| 331 : |
|
|
// funcval = trialf;
|
| 332 : |
|
|
// nacc++;
|
| 333 : |
|
|
// nacp[param[l]]++; //JMB - not sure about this ...
|
| 334 : |
|
|
//
|
| 335 : |
|
|
// } else {
|
| 336 : |
|
|
// //Accept according to metropolis condition
|
| 337 : |
|
|
// p = expRep((trialf - funcval) / t);
|
| 338 : |
|
|
// pp = randomNumber(&seedM);
|
| 339 : |
|
|
// if (pp < p) {
|
| 340 : |
|
|
// //Accept point
|
| 341 : |
|
|
// for (i = 0; i < nvars; i++)
|
| 342 : |
|
|
// x[i] = trialx[i];
|
| 343 : |
|
|
// funcval = trialf;
|
| 344 : |
|
|
// naccmet++;
|
| 345 : |
|
|
// nacp[param[l]]++;
|
| 346 : |
|
|
// } else {
|
| 347 : |
|
|
// //Reject point
|
| 348 : |
|
|
// nrej++;
|
| 349 : |
|
|
// }
|
| 350 : |
|
|
// }
|
| 351 : |
|
|
// // JMB added check for really silly values
|
| 352 : |
|
|
// if (isZero(trialf)) {
|
| 353 : |
|
|
// handle.logMessage(LOGINFO, "Error in Simulated Annealing optimisation after", iters, "function evaluations, f(x) = 0");
|
| 354 : |
|
|
// converge = -1;
|
| 355 : |
|
|
// return;
|
| 356 : |
|
|
// }
|
| 357 : |
|
|
//
|
| 358 : |
|
|
// //If greater than any other point, record as new optimum
|
| 359 : |
|
|
// if ((trialf > fopt) && (trialf == trialf)) {
|
| 360 : |
|
|
// for (i = 0; i < nvars; i++)
|
| 361 : |
|
|
// bestx[i] = trialx[i];
|
| 362 : |
|
|
// fopt = trialf;
|
| 363 : |
|
|
//
|
| 364 : |
|
|
// if (scale) {
|
| 365 : |
|
|
// for (i = 0; i < nvars; i++)
|
| 366 : |
|
|
// scalex[i] = bestx[i] * init[i];
|
| 367 : |
|
|
// EcoSystem->storeVariables(-fopt, scalex);
|
| 368 : |
|
|
// } else
|
| 369 : |
|
|
// EcoSystem->storeVariables(-fopt, bestx);
|
| 370 : |
|
|
//
|
| 371 : |
|
|
// handle.logMessage(LOGINFO, "\nNew optimum found after", iters, "function evaluations");
|
| 372 : |
|
|
// handle.logMessage(LOGINFO, "The likelihood score is", -fopt, "at the point");
|
| 373 : |
|
|
// EcoSystem->writeBestValues();
|
| 374 : |
|
|
// }
|
| 375 : |
|
|
// }
|
| 376 : |
|
|
// }
|
| 377 : |
|
|
//
|
| 378 : |
|
|
// //Adjust vm so that approximately half of all evaluations are accepted
|
| 379 : |
|
|
// for (i = 0; i < nvars; i++) {
|
| 380 : |
|
|
// ratio = nsdiv * nacp[i];
|
| 381 : |
|
|
// nacp[i] = 0;
|
| 382 : |
|
|
// if (ratio > uratio) {
|
| 383 : |
|
|
// vm[i] = vm[i] * (1.0 + cs * (ratio - uratio));
|
| 384 : |
|
|
// } else if (ratio < lratio) {
|
| 385 : |
|
|
// vm[i] = vm[i] / (1.0 + cs * (lratio - ratio));
|
| 386 : |
|
|
// }
|
| 387 : |
|
|
//
|
| 388 : |
|
|
// if (vm[i] < rathersmall)
|
| 389 : |
|
|
// vm[i] = rathersmall;
|
| 390 : |
|
|
// if (vm[i] > (upperb[i] - lowerb[i]))
|
| 391 : |
|
|
// vm[i] = upperb[i] - lowerb[i];
|
| 392 : |
|
|
// }
|
| 393 : |
|
|
// }
|
| 394 : |
|
|
//
|
| 395 : |
|
|
// //Check termination criteria
|
| 396 : |
|
|
// for (i = tempcheck - 1; i > 0; i--)
|
| 397 : |
|
|
// fstar[i] = fstar[i - 1];
|
| 398 : |
|
|
// fstar[0] = funcval;
|
| 399 : |
|
|
//
|
| 400 : |
|
|
// quit = 0;
|
| 401 : |
|
|
// if (fabs(fopt - funcval) < simanneps) {
|
| 402 : |
|
|
// quit = 1;
|
| 403 : |
|
|
// for (i = 0; i < tempcheck - 1; i++)
|
| 404 : |
|
|
// if (fabs(fstar[i + 1] - fstar[i]) > simanneps)
|
| 405 : |
|
|
// quit = 0;
|
| 406 : |
|
|
// }
|
| 407 : |
|
|
//
|
| 408 : |
|
|
// handle.logMessage(LOGINFO, "Checking convergence criteria after", iters, "function evaluations ...");
|
| 409 : |
|
|
//
|
| 410 : |
|
|
// //Terminate SA if appropriate
|
| 411 : |
|
|
// if (quit) {
|
| 412 : |
|
|
// handle.logMessage(LOGINFO, "\nStopping Simulated Annealing optimisation algorithm\n");
|
| 413 : |
|
|
// handle.logMessage(LOGINFO, "The optimisation stopped after", iters, "function evaluations");
|
| 414 : |
|
|
// handle.logMessage(LOGINFO, "The temperature was reduced to", t);
|
| 415 : |
|
|
// handle.logMessage(LOGINFO, "The optimisation stopped because an optimum was found for this run");
|
| 416 : |
|
|
// handle.logMessage(LOGINFO, "Number of directly accepted points", nacc);
|
| 417 : |
|
|
// handle.logMessage(LOGINFO, "Number of metropolis accepted points", naccmet);
|
| 418 : |
|
|
// handle.logMessage(LOGINFO, "Number of rejected points", nrej);
|
| 419 : |
|
|
//
|
| 420 : |
|
|
// converge = 1;
|
| 421 : |
|
|
// score = EcoSystem->SimulateAndUpdate(bestx);
|
| 422 : |
|
|
// handle.logMessage(LOGINFO, "\nSimulated Annealing finished with a likelihood score of", score);
|
| 423 : |
|
|
// return;
|
| 424 : |
|
|
// }
|
| 425 : |
|
|
//
|
| 426 : |
|
|
// //If termination criteria is not met, prepare for another loop.
|
| 427 : |
|
|
// t *= rt;
|
| 428 : |
|
|
// if (t < rathersmall)
|
| 429 : |
|
|
// t = rathersmall; //JMB make sure temperature doesnt get too small
|
| 430 : |
|
|
//
|
| 431 : |
|
|
// handle.logMessage(LOGINFO, "Reducing the temperature to", t);
|
| 432 : |
|
|
// funcval = fopt;
|
| 433 : |
|
|
// for (i = 0; i < nvars; i++)
|
| 434 : |
|
|
// x[i] = bestx[i];
|
| 435 : |
|
|
// }
|
| 436 : |
|
|
//}
|
| 437 : |
agomez |
1 |
|
| 438 : |
ulcessvp |
11 |
|
| 439 : |
ulcessvp |
15 |
#ifdef SPECULATIVE
|
| 440 : |
ulcessvp |
11 |
void OptInfoSimann::OptimiseLikelihoodOMP() {
|
| 441 : |
|
|
|
| 442 : |
agomez |
1 |
//set initial values
|
| 443 : |
|
|
int nacc = 0; //The number of accepted function evaluations
|
| 444 : |
|
|
int nrej = 0; //The number of rejected function evaluations
|
| 445 : |
|
|
int naccmet = 0; //The number of metropolis accepted function evaluations
|
| 446 : |
|
|
|
| 447 : |
|
|
double tmp, p, pp, ratio, nsdiv;
|
| 448 : |
|
|
double fopt, funcval, trialf;
|
| 449 : |
ulcessvp |
14 |
int a, i, j, k, l, quit;
|
| 450 : |
agomez |
1 |
int rchange, rcheck, rnumber; //Used to randomise the order of the parameters
|
| 451 : |
|
|
|
| 452 : |
ulcessvp |
14 |
// store the info of the different threads
|
| 453 : |
ulcessvp |
11 |
struct Storage {
|
| 454 : |
|
|
DoubleVector trialx;
|
| 455 : |
|
|
double newLikelihood;
|
| 456 : |
|
|
};
|
| 457 : |
|
|
|
| 458 : |
agomez |
1 |
handle.logMessage(LOGINFO, "\nStarting Simulated Annealing optimisation algorithm\n");
|
| 459 : |
|
|
int nvars = EcoSystem->numOptVariables();
|
| 460 : |
|
|
DoubleVector x(nvars);
|
| 461 : |
|
|
DoubleVector init(nvars);
|
| 462 : |
|
|
DoubleVector trialx(nvars, 0.0);
|
| 463 : |
|
|
DoubleVector bestx(nvars);
|
| 464 : |
|
|
DoubleVector scalex(nvars);
|
| 465 : |
|
|
DoubleVector lowerb(nvars);
|
| 466 : |
|
|
DoubleVector upperb(nvars);
|
| 467 : |
|
|
DoubleVector fstar(tempcheck);
|
| 468 : |
|
|
DoubleVector vm(nvars, vminit);
|
| 469 : |
|
|
IntVector param(nvars, 0);
|
| 470 : |
|
|
IntVector nacp(nvars, 0);
|
| 471 : |
|
|
|
| 472 : |
|
|
EcoSystem->resetVariables(); //JMB need to reset variables in case they have been scaled
|
| 473 : |
ulcessvp |
14 |
if (scale) {
|
| 474 : |
|
|
EcoSystem->scaleVariables();
|
| 475 : |
|
|
int numThr = omp_get_max_threads ( );
|
| 476 : |
|
|
for(i = 0; i < numThr; i++) // scale the variables for the ecosystem of every thread
|
| 477 : |
|
|
EcoSystems[i]->scaleVariables();
|
| 478 : |
|
|
}
|
| 479 : |
agomez |
1 |
EcoSystem->getOptScaledValues(x);
|
| 480 : |
|
|
EcoSystem->getOptLowerBounds(lowerb);
|
| 481 : |
|
|
EcoSystem->getOptUpperBounds(upperb);
|
| 482 : |
|
|
EcoSystem->getOptInitialValues(init);
|
| 483 : |
|
|
|
| 484 : |
ulcessvp |
11 |
for (i = 0; i < nvars; ++i) {
|
| 485 : |
agomez |
1 |
bestx[i] = x[i];
|
| 486 : |
|
|
param[i] = i;
|
| 487 : |
|
|
}
|
| 488 : |
|
|
|
| 489 : |
|
|
if (scale) {
|
| 490 : |
ulcessvp |
11 |
for (i = 0; i < nvars; ++i) {
|
| 491 : |
agomez |
1 |
scalex[i] = x[i];
|
| 492 : |
|
|
// Scaling the bounds, because the parameters are scaled
|
| 493 : |
|
|
lowerb[i] = lowerb[i] / init[i];
|
| 494 : |
|
|
upperb[i] = upperb[i] / init[i];
|
| 495 : |
|
|
if (lowerb[i] > upperb[i]) {
|
| 496 : |
|
|
tmp = lowerb[i];
|
| 497 : |
|
|
lowerb[i] = upperb[i];
|
| 498 : |
|
|
upperb[i] = tmp;
|
| 499 : |
|
|
}
|
| 500 : |
|
|
}
|
| 501 : |
|
|
}
|
| 502 : |
|
|
|
| 503 : |
|
|
//funcval is the function value at x
|
| 504 : |
|
|
funcval = EcoSystem->SimulateAndUpdate(x);
|
| 505 : |
|
|
if (funcval != funcval) { //check for NaN
|
| 506 : |
|
|
handle.logMessage(LOGINFO, "Error starting Simulated Annealing optimisation with f(x) = infinity");
|
| 507 : |
|
|
converge = -1;
|
| 508 : |
|
|
iters = 1;
|
| 509 : |
|
|
return;
|
| 510 : |
|
|
}
|
| 511 : |
|
|
|
| 512 : |
|
|
//the function is to be minimised so switch the sign of funcval (and trialf)
|
| 513 : |
|
|
funcval = -funcval;
|
| 514 : |
|
|
nacc++;
|
| 515 : |
|
|
cs /= lratio; //JMB save processing time
|
| 516 : |
|
|
nsdiv = 1.0 / ns;
|
| 517 : |
|
|
fopt = funcval;
|
| 518 : |
ulcessvp |
11 |
for (i = 0; i < tempcheck; ++i)
|
| 519 : |
agomez |
1 |
fstar[i] = funcval;
|
| 520 : |
|
|
|
| 521 : |
ulcessvp |
11 |
|
| 522 : |
|
|
|
| 523 : |
|
|
int numThr = omp_get_max_threads ( );
|
| 524 : |
|
|
int bestId=0;
|
| 525 : |
|
|
int ini=0;
|
| 526 : |
|
|
|
| 527 : |
|
|
Storage* storage = new Storage[numThr];
|
| 528 : |
|
|
|
| 529 : |
|
|
for (i=0; i<numThr; ++i)
|
| 530 : |
|
|
storage[i].trialx = trialx;
|
| 531 : |
|
|
|
| 532 : |
|
|
|
| 533 : |
ulcessvp |
14 |
int aux; //store the number of evaluations that are not useful for the algorithm
|
| 534 : |
|
|
|
| 535 : |
|
|
DoubleVector vns(nvars, 0); //vector of ns
|
| 536 : |
ulcessvp |
11 |
int ns_ = ceil(numThr/2.);
|
| 537 : |
|
|
double res;
|
| 538 : |
|
|
aux=0;
|
| 539 : |
ulcessvp |
14 |
//Start the main loop. Note that it terminates if
|
| 540 : |
|
|
//(i) the algorithm succesfully optimises the function or
|
| 541 : |
|
|
//(ii) there are too many function evaluations
|
| 542 : |
agomez |
1 |
while (1) {
|
| 543 : |
ulcessvp |
11 |
for (a = 0; a < nt; ++a) {
|
| 544 : |
agomez |
1 |
//Randomize the order of the parameters once in a while, to avoid
|
| 545 : |
|
|
//the order having an influence on which changes are accepted
|
| 546 : |
|
|
rchange = 0;
|
| 547 : |
|
|
while (rchange < nvars) {
|
| 548 : |
ulcessvp |
11 |
rnumber = rand_r(&seedP) % nvars;
|
| 549 : |
agomez |
1 |
rcheck = 1;
|
| 550 : |
ulcessvp |
11 |
for (i = 0; i < rchange; ++i)
|
| 551 : |
agomez |
1 |
if (param[i] == rnumber)
|
| 552 : |
|
|
rcheck = 0;
|
| 553 : |
|
|
if (rcheck) {
|
| 554 : |
|
|
param[rchange] = rnumber;
|
| 555 : |
|
|
rchange++;
|
| 556 : |
|
|
}
|
| 557 : |
|
|
}
|
| 558 : |
|
|
|
| 559 : |
|
|
|
| 560 : |
ulcessvp |
11 |
for (j = 0; j < (ns*ns_); ++j) {
|
| 561 : |
|
|
for (l = ini; l < nvars; l+=numThr) {
|
| 562 : |
|
|
for (i=0; i<numThr;++i)
|
| 563 : |
|
|
{
|
| 564 : |
|
|
if ((l+i) < nvars)
|
| 565 : |
|
|
newValue(nvars, l+i, param, storage[i].trialx, x, lowerb, upperb, vm);
|
| 566 : |
|
|
else {
|
| 567 : |
|
|
newValue(nvars, ini, param, storage[i].trialx, x, lowerb, upperb, vm);
|
| 568 : |
|
|
ini++;
|
| 569 : |
|
|
if (ini >= nvars)
|
| 570 : |
|
|
ini=0;
|
| 571 : |
|
|
}
|
| 572 : |
|
|
}
|
| 573 : |
agomez |
1 |
|
| 574 : |
ulcessvp |
11 |
# pragma omp parallel private(res)
|
| 575 : |
|
|
{
|
| 576 : |
|
|
//Evaluate the function with the trial point trialx and return as -trialf
|
| 577 : |
|
|
int id = omp_get_thread_num ();
|
| 578 : |
|
|
res = EcoSystems[id]->SimulateAndUpdate(storage[id].trialx);
|
| 579 : |
|
|
storage[id].newLikelihood = -res;
|
| 580 : |
|
|
}
|
| 581 : |
|
|
//best value from omp
|
| 582 : |
|
|
trialf = storage[0].newLikelihood;
|
| 583 : |
|
|
bestId=0;
|
| 584 : |
|
|
for (i=0;i<numThr;++i)
|
| 585 : |
|
|
{
|
| 586 : |
|
|
if (storage[i].newLikelihood > trialf)
|
| 587 : |
|
|
{
|
| 588 : |
|
|
trialf=storage[i].newLikelihood;
|
| 589 : |
|
|
bestId=i;
|
| 590 : |
|
|
}
|
| 591 : |
|
|
k = param[(l+i)%nvars];
|
| 592 : |
agomez |
1 |
|
| 593 : |
ulcessvp |
11 |
if ((storage[i].newLikelihood - funcval) > verysmall)
|
| 594 : |
|
|
{
|
| 595 : |
|
|
nacp[k]++;
|
| 596 : |
|
|
aux++;
|
| 597 : |
|
|
vns[k]++;
|
| 598 : |
|
|
}
|
| 599 : |
|
|
else {
|
| 600 : |
|
|
//Accept according to metropolis condition
|
| 601 : |
|
|
p = expRep((storage[i].newLikelihood - funcval) / t);
|
| 602 : |
|
|
pp = randomNumber(&seedM);
|
| 603 : |
|
|
if (pp < p)
|
| 604 : |
|
|
aux++;
|
| 605 : |
|
|
else {
|
| 606 : |
|
|
vns[k]++;
|
| 607 : |
|
|
nrej++;
|
| 608 : |
|
|
}
|
| 609 : |
|
|
}
|
| 610 : |
agomez |
1 |
|
| 611 : |
ulcessvp |
11 |
if (vns[k] >= ns) {
|
| 612 : |
|
|
ratio = nsdiv * nacp[k];
|
| 613 : |
|
|
nacp[k] = 0;
|
| 614 : |
|
|
if (ratio > uratio) {
|
| 615 : |
|
|
vm[k] = vm[k] * (1.0 + cs * (ratio - uratio));
|
| 616 : |
|
|
} else if (ratio < lratio) {
|
| 617 : |
|
|
vm[k] = vm[k] / (1.0 + cs * (lratio - ratio));
|
| 618 : |
|
|
}
|
| 619 : |
|
|
if (vm[k] < rathersmall){
|
| 620 : |
|
|
vm[k] = rathersmall;
|
| 621 : |
|
|
}
|
| 622 : |
|
|
if (vm[k] > (upperb[k] - lowerb[k]))
|
| 623 : |
|
|
{
|
| 624 : |
|
|
vm[k] = upperb[k] - lowerb[k];
|
| 625 : |
|
|
}
|
| 626 : |
|
|
vns[k]=0;
|
| 627 : |
|
|
}
|
| 628 : |
|
|
}
|
| 629 : |
|
|
aux--;
|
| 630 : |
|
|
iters = (EcoSystems[bestId]->getFuncEval() * numThr) -aux;
|
| 631 : |
|
|
if (iters > simanniter) {
|
| 632 : |
|
|
handle.logMessage(LOGINFO, "\nStopping Simulated Annealing optimisation algorithm\n");
|
| 633 : |
|
|
handle.logMessage(LOGINFO, "The optimisation stopped after", iters, "function evaluations");
|
| 634 : |
|
|
handle.logMessage(LOGINFO, "The temperature was reduced to", t);
|
| 635 : |
|
|
handle.logMessage(LOGINFO, "The optimisation stopped because the maximum number of function evaluations");
|
| 636 : |
|
|
handle.logMessage(LOGINFO, "was reached and NOT because an optimum was found for this run");
|
| 637 : |
|
|
handle.logMessage(LOGINFO, "Number of directly accepted points", nacc);
|
| 638 : |
|
|
handle.logMessage(LOGINFO, "Number of metropolis accepted points", naccmet);
|
| 639 : |
|
|
handle.logMessage(LOGINFO, "Number of rejected points", nrej);
|
| 640 : |
agomez |
1 |
|
| 641 : |
ulcessvp |
11 |
score = EcoSystem->SimulateAndUpdate(bestx);
|
| 642 : |
|
|
handle.logMessage(LOGINFO, "\nSimulated Annealing finished with a likelihood score of", score);
|
| 643 : |
|
|
delete[] storage;
|
| 644 : |
|
|
return;
|
| 645 : |
agomez |
1 |
}
|
| 646 : |
|
|
|
| 647 : |
|
|
//Accept the new point if the new function value better
|
| 648 : |
|
|
if ((trialf - funcval) > verysmall) {
|
| 649 : |
ulcessvp |
11 |
for (i = 0; i < nvars; ++i)
|
| 650 : |
|
|
x[i] = storage[bestId].trialx[i];
|
| 651 : |
agomez |
1 |
funcval = trialf;
|
| 652 : |
|
|
nacc++;
|
| 653 : |
|
|
} else {
|
| 654 : |
|
|
//Accept according to metropolis condition
|
| 655 : |
|
|
p = expRep((trialf - funcval) / t);
|
| 656 : |
ulcessvp |
11 |
pp = randomNumber(&seedP);
|
| 657 : |
agomez |
1 |
if (pp < p) {
|
| 658 : |
|
|
//Accept point
|
| 659 : |
ulcessvp |
11 |
for (i = 0; i < nvars; ++i)
|
| 660 : |
|
|
x[i] = storage[bestId].trialx[i];
|
| 661 : |
agomez |
1 |
funcval = trialf;
|
| 662 : |
|
|
naccmet++;
|
| 663 : |
ulcessvp |
11 |
nacp[param[(l+bestId)%nvars]]++;
|
| 664 : |
agomez |
1 |
} else {
|
| 665 : |
|
|
//Reject point
|
| 666 : |
|
|
nrej++;
|
| 667 : |
|
|
}
|
| 668 : |
|
|
}
|
| 669 : |
|
|
// JMB added check for really silly values
|
| 670 : |
|
|
if (isZero(trialf)) {
|
| 671 : |
|
|
handle.logMessage(LOGINFO, "Error in Simulated Annealing optimisation after", iters, "function evaluations, f(x) = 0");
|
| 672 : |
|
|
converge = -1;
|
| 673 : |
ulcessvp |
11 |
delete[] storage;
|
| 674 : |
agomez |
1 |
return;
|
| 675 : |
|
|
}
|
| 676 : |
|
|
|
| 677 : |
|
|
//If greater than any other point, record as new optimum
|
| 678 : |
|
|
if ((trialf > fopt) && (trialf == trialf)) {
|
| 679 : |
ulcessvp |
11 |
for (i = 0; i < nvars; ++i)
|
| 680 : |
|
|
bestx[i] = storage[bestId].trialx[i];
|
| 681 : |
agomez |
1 |
fopt = trialf;
|
| 682 : |
|
|
|
| 683 : |
|
|
if (scale) {
|
| 684 : |
ulcessvp |
11 |
for (i = 0; i < nvars; ++i)
|
| 685 : |
agomez |
1 |
scalex[i] = bestx[i] * init[i];
|
| 686 : |
|
|
EcoSystem->storeVariables(-fopt, scalex);
|
| 687 : |
|
|
} else
|
| 688 : |
ulcessvp |
11 |
EcoSystem->storeVariables(-fopt, bestx);
|
| 689 : |
agomez |
1 |
|
| 690 : |
|
|
handle.logMessage(LOGINFO, "\nNew optimum found after", iters, "function evaluations");
|
| 691 : |
|
|
handle.logMessage(LOGINFO, "The likelihood score is", -fopt, "at the point");
|
| 692 : |
|
|
EcoSystem->writeBestValues();
|
| 693 : |
|
|
}
|
| 694 : |
|
|
}
|
| 695 : |
|
|
}
|
| 696 : |
|
|
}
|
| 697 : |
|
|
|
| 698 : |
|
|
//Check termination criteria
|
| 699 : |
|
|
for (i = tempcheck - 1; i > 0; i--)
|
| 700 : |
|
|
fstar[i] = fstar[i - 1];
|
| 701 : |
|
|
fstar[0] = funcval;
|
| 702 : |
|
|
|
| 703 : |
|
|
quit = 0;
|
| 704 : |
|
|
if (fabs(fopt - funcval) < simanneps) {
|
| 705 : |
|
|
quit = 1;
|
| 706 : |
ulcessvp |
11 |
for (i = 0; i < tempcheck - 1; ++i)
|
| 707 : |
agomez |
1 |
if (fabs(fstar[i + 1] - fstar[i]) > simanneps)
|
| 708 : |
|
|
quit = 0;
|
| 709 : |
|
|
}
|
| 710 : |
|
|
|
| 711 : |
|
|
handle.logMessage(LOGINFO, "Checking convergence criteria after", iters, "function evaluations ...");
|
| 712 : |
|
|
|
| 713 : |
|
|
//Terminate SA if appropriate
|
| 714 : |
|
|
if (quit) {
|
| 715 : |
|
|
handle.logMessage(LOGINFO, "\nStopping Simulated Annealing optimisation algorithm\n");
|
| 716 : |
|
|
handle.logMessage(LOGINFO, "The optimisation stopped after", iters, "function evaluations");
|
| 717 : |
|
|
handle.logMessage(LOGINFO, "The temperature was reduced to", t);
|
| 718 : |
|
|
handle.logMessage(LOGINFO, "The optimisation stopped because an optimum was found for this run");
|
| 719 : |
|
|
handle.logMessage(LOGINFO, "Number of directly accepted points", nacc);
|
| 720 : |
|
|
handle.logMessage(LOGINFO, "Number of metropolis accepted points", naccmet);
|
| 721 : |
|
|
handle.logMessage(LOGINFO, "Number of rejected points", nrej);
|
| 722 : |
|
|
|
| 723 : |
|
|
converge = 1;
|
| 724 : |
|
|
score = EcoSystem->SimulateAndUpdate(bestx);
|
| 725 : |
|
|
handle.logMessage(LOGINFO, "\nSimulated Annealing finished with a likelihood score of", score);
|
| 726 : |
ulcessvp |
11 |
delete[] storage;
|
| 727 : |
agomez |
1 |
return;
|
| 728 : |
|
|
}
|
| 729 : |
|
|
|
| 730 : |
|
|
//If termination criteria is not met, prepare for another loop.
|
| 731 : |
|
|
t *= rt;
|
| 732 : |
|
|
if (t < rathersmall)
|
| 733 : |
|
|
t = rathersmall; //JMB make sure temperature doesnt get too small
|
| 734 : |
|
|
|
| 735 : |
|
|
handle.logMessage(LOGINFO, "Reducing the temperature to", t);
|
| 736 : |
|
|
funcval = fopt;
|
| 737 : |
ulcessvp |
11 |
for (i = 0; i < nvars; ++i)
|
| 738 : |
agomez |
1 |
x[i] = bestx[i];
|
| 739 : |
|
|
}
|
| 740 : |
|
|
}
|
| 741 : |
ulcessvp |
11 |
#endif
|
| 742 : |
|
|
|
| 743 : |
ulcessvp |
14 |
// calcule a new point
|
| 744 : |
ulcessvp |
11 |
void OptInfoSimann::newValue(int nvars, int l, IntVector& param, DoubleVector& trialx,
|
| 745 : |
|
|
DoubleVector& x, DoubleVector& lowerb, DoubleVector& upperb, DoubleVector& vm)
|
| 746 : |
|
|
{
|
| 747 : |
|
|
int i, k;
|
| 748 : |
|
|
for (i = 0; i < nvars; ++i) {
|
| 749 : |
|
|
if (i == param[l]) {
|
| 750 : |
|
|
trialx[i] = x[i] + ((randomNumber(&seed) * 2.0) - 1.0) * vm[i];
|
| 751 : |
|
|
|
| 752 : |
|
|
//If trialx is out of bounds, try again until we find a point that is OK
|
| 753 : |
|
|
if ((trialx[i] < lowerb[i]) || (trialx[i] > upperb[i])) {
|
| 754 : |
|
|
//JMB - this used to just select a random point between the bounds
|
| 755 : |
|
|
k = 0;
|
| 756 : |
|
|
while ((trialx[i] < lowerb[i]) || (trialx[i] > upperb[i])) {
|
| 757 : |
|
|
trialx[i] = x[i] + ((randomNumber(&seed) * 2.0) - 1.0) * vm[i];
|
| 758 : |
|
|
k++;
|
| 759 : |
|
|
if (k > 10) //we've had 10 tries to find a point neatly, so give up
|
| 760 : |
|
|
trialx[i] = lowerb[i] + (upperb[i] - lowerb[i]) * randomNumber(&seed);
|
| 761 : |
|
|
}
|
| 762 : |
|
|
}
|
| 763 : |
|
|
} else
|
| 764 : |
|
|
trialx[i] = x[i];
|
| 765 : |
|
|
}
|
| 766 : |
|
|
}
|
| 767 : |
|
|
|
| 768 : |
ulcessvp |
14 |
/*################################################################################
|
| 769 : |
|
|
* code use in the template (seq_optimize_template.h)
|
| 770 : |
|
|
################################################################################*/
|
| 771 : |
ulcessvp |
11 |
|
| 772 : |
ulcessvp |
14 |
|
| 773 : |
ulcessvp |
11 |
//Generate trialx, the trial value of x
|
| 774 : |
|
|
void newValue(int nvars, int l, IntVector& param, DoubleVector& trialx,
|
| 775 : |
|
|
DoubleVector& x, DoubleVector& lowerb, DoubleVector& upperb, DoubleVector& vm, unsigned* seed)
|
| 776 : |
|
|
{
|
| 777 : |
|
|
int i, k;
|
| 778 : |
|
|
for (i = 0; i < nvars; ++i) {
|
| 779 : |
|
|
if (i == param[l]) {
|
| 780 : |
|
|
trialx[i] = x[i] + ((randomNumber(&*seed) * 2.0) - 1.0) * vm[i];
|
| 781 : |
|
|
|
| 782 : |
|
|
//If trialx is out of bounds, try again until we find a point that is OK
|
| 783 : |
|
|
if ((trialx[i] < lowerb[i]) || (trialx[i] > upperb[i])) {
|
| 784 : |
|
|
//JMB - this used to just select a random point between the bounds
|
| 785 : |
|
|
k = 0;
|
| 786 : |
|
|
while ((trialx[i] < lowerb[i]) || (trialx[i] > upperb[i])) {
|
| 787 : |
|
|
trialx[i] = x[i] + ((randomNumber(&*seed) * 2.0) - 1.0) * vm[i];
|
| 788 : |
|
|
k++;
|
| 789 : |
|
|
if (k > 10) //we've had 10 tries to find a point neatly, so give up
|
| 790 : |
|
|
trialx[i] = lowerb[i] + (upperb[i] - lowerb[i]) * randomNumber(&*seed);
|
| 791 : |
|
|
}
|
| 792 : |
|
|
}
|
| 793 : |
|
|
} else
|
| 794 : |
|
|
trialx[i] = x[i];
|
| 795 : |
|
|
}
|
| 796 : |
|
|
}
|
| 797 : |
|
|
|
| 798 : |
|
|
void buildNewParams_f(Siman& seed, DoubleVector& params) {
|
| 799 : |
|
|
|
| 800 : |
|
|
newValue(seed.getNvars(), seed.getL(), seed.getParam(), params, seed.getX(),
|
| 801 : |
|
|
seed.getLowerb(), seed.getUpperb(), seed.getVm(), seed.getSeed());
|
| 802 : |
|
|
|
| 803 : |
|
|
}
|
| 804 : |
|
|
|
| 805 : |
|
|
/// Represents the function that computes how good the parameters are
|
| 806 : |
|
|
double evaluate_f(const DoubleVector& params) {
|
| 807 : |
|
|
double trialf;
|
| 808 : |
|
|
#ifndef NO_OPENMP
|
| 809 : |
|
|
int id = omp_get_thread_num ();
|
| 810 : |
|
|
trialf = EcoSystems[id]->SimulateAndUpdate(params);
|
| 811 : |
|
|
#else
|
| 812 : |
|
|
trialf = EcoSystem->SimulateAndUpdate(params);
|
| 813 : |
|
|
#endif
|
| 814 : |
|
|
return -trialf;
|
| 815 : |
|
|
}
|
| 816 : |
|
|
|
| 817 : |
|
|
struct ControlClass {
|
| 818 : |
|
|
|
| 819 : |
|
|
void adjustVm(Siman& seed) {
|
| 820 : |
|
|
//Adjust vm so that approximately half of all evaluations are accepted
|
| 821 : |
|
|
int i;
|
| 822 : |
|
|
double ratio, nsdiv = seed.getNsdiv();
|
| 823 : |
|
|
|
| 824 : |
|
|
DoubleVector vm = seed.getVm();
|
| 825 : |
|
|
DoubleVector upperb = seed.getUpperb();
|
| 826 : |
|
|
DoubleVector lowerb = seed.getLowerb();
|
| 827 : |
|
|
|
| 828 : |
|
|
double uratio = seed.getUratio();
|
| 829 : |
|
|
double lratio = seed.getLratio();
|
| 830 : |
|
|
|
| 831 : |
|
|
for (i = 0; i < seed.getNvars(); i++) {
|
| 832 : |
|
|
ratio = nsdiv * seed.getNacp(i);
|
| 833 : |
|
|
seed.setNacp(i,0);
|
| 834 : |
|
|
if (ratio > uratio) {
|
| 835 : |
|
|
(vm)[i] = (vm)[i] * (1.0 + seed.getCs() * (ratio - seed.getUratio()));
|
| 836 : |
|
|
} else if (ratio < lratio) {
|
| 837 : |
|
|
(vm)[i] = (vm)[i] / (1.0 + seed.getCs() * (seed.getLratio() - ratio));
|
| 838 : |
|
|
}
|
| 839 : |
|
|
|
| 840 : |
|
|
if ((vm)[i] < rathersmall)
|
| 841 : |
|
|
(vm)[i] = rathersmall;
|
| 842 : |
|
|
if ((vm)[i] > (upperb[i] - lowerb[i]))
|
| 843 : |
|
|
(vm)[i] = upperb[i] - lowerb[i];
|
| 844 : |
|
|
}
|
| 845 : |
|
|
seed.setVm(vm);
|
| 846 : |
|
|
}
|
| 847 : |
|
|
|
| 848 : |
|
|
void temperature(Siman& seed, DoubleVector& x){
|
| 849 : |
|
|
int i;
|
| 850 : |
|
|
double t = seed.getT();
|
| 851 : |
|
|
t *= seed.getRt();
|
| 852 : |
|
|
if (t < rathersmall)
|
| 853 : |
|
|
t = rathersmall; //JMB make sure temperature doesnt get too small
|
| 854 : |
|
|
|
| 855 : |
|
|
handle.logMessage(LOGINFO, "Reducing the temperature to", t);
|
| 856 : |
|
|
seed.setT(t);
|
| 857 : |
|
|
|
| 858 : |
|
|
DoubleVector* bestx = seed.getBestx();
|
| 859 : |
|
|
|
| 860 : |
|
|
for (i = 0; i < seed.getNvars(); i++)
|
| 861 : |
|
|
x[i] = (*bestx)[i];
|
| 862 : |
|
|
}
|
| 863 : |
|
|
|
| 864 : |
|
|
void optimum(double trialf, double &fopt, int iters, DoubleVector trialx,
|
| 865 : |
|
|
DoubleVector init, Siman siman){
|
| 866 : |
|
|
//If greater than any other point, record as new optimum
|
| 867 : |
|
|
int i, nvars = siman.getNvars();
|
| 868 : |
|
|
DoubleVector scalex(nvars);
|
| 869 : |
|
|
DoubleVector* bestx = siman.getBestx();
|
| 870 : |
|
|
|
| 871 : |
|
|
if ((trialf > fopt) && (trialf == trialf)) {
|
| 872 : |
|
|
for (i = 0; i < nvars; i++)
|
| 873 : |
|
|
(*bestx)[i] = trialx[i];
|
| 874 : |
|
|
fopt = trialf;
|
| 875 : |
|
|
|
| 876 : |
|
|
if (siman.getScale()) {
|
| 877 : |
|
|
for (i = 0; i < nvars; i++)
|
| 878 : |
|
|
scalex[i] = (*bestx)[i] * init[i];
|
| 879 : |
|
|
EcoSystem->storeVariables(-fopt, scalex);
|
| 880 : |
|
|
} else
|
| 881 : |
|
|
EcoSystem->storeVariables(-fopt, (*bestx));
|
| 882 : |
|
|
|
| 883 : |
|
|
handle.logMessage(LOGINFO, "\nNew optimum found after", iters, "function evaluations");
|
| 884 : |
|
|
handle.logMessage(LOGINFO, "The likelihood score is", -fopt, "at the point");
|
| 885 : |
|
|
EcoSystem->writeBestValues();
|
| 886 : |
|
|
}
|
| 887 : |
|
|
}
|
| 888 : |
|
|
|
| 889 : |
|
|
/**
|
| 890 : |
|
|
@brief Decides wheter the current item evaluated must be chosen as new optimum
|
| 891 : |
|
|
@param funcval value for old optimum
|
| 892 : |
|
|
@param trialf value for current item evaluated
|
| 893 : |
|
|
*/
|
| 894 : |
|
|
bool mustAccept(double funcval, double trialf, Siman &siman, int iters) {
|
| 895 : |
|
|
//Accept the new point if the new function value better
|
| 896 : |
|
|
bool ret = true;
|
| 897 : |
|
|
int i;
|
| 898 : |
|
|
int aux = siman.getParam()[siman.getL()];
|
| 899 : |
|
|
if ((trialf - funcval) > verysmall) {
|
| 900 : |
|
|
siman.incrementNacp(aux);
|
| 901 : |
|
|
siman.incrementNacc();
|
| 902 : |
|
|
//JMB - not sure about this ...
|
| 903 : |
|
|
} else {
|
| 904 : |
|
|
double p, pp;
|
| 905 : |
|
|
//Accept according to metropolis condition
|
| 906 : |
|
|
|
| 907 : |
|
|
p = expRep((trialf - funcval) / siman.getT());
|
| 908 : |
|
|
pp = randomNumber(siman.getSeedM());
|
| 909 : |
|
|
if (pp < p) {
|
| 910 : |
|
|
siman.incrementNacp(aux);
|
| 911 : |
|
|
siman.incrementNaccmet();
|
| 912 : |
|
|
// //Accept point
|
| 913 : |
|
|
} else {
|
| 914 : |
|
|
//Reject point
|
| 915 : |
|
|
ret = false;
|
| 916 : |
|
|
siman.incrementNrej();
|
| 917 : |
|
|
}
|
| 918 : |
|
|
}
|
| 919 : |
|
|
// JMB added check for really silly values
|
| 920 : |
|
|
if (isZero(trialf)) {
|
| 921 : |
|
|
handle.logMessage(LOGINFO, "Error in Simulated Annealing optimisation after",
|
| 922 : |
|
|
iters, "function evaluations, f(x) = 0");
|
| 923 : |
|
|
siman.setConverge(-1);
|
| 924 : |
|
|
return false;
|
| 925 : |
|
|
}
|
| 926 : |
|
|
|
| 927 : |
|
|
siman.incrementL();
|
| 928 : |
|
|
if (siman.getL() == 0)
|
| 929 : |
|
|
siman.incrementNS();
|
| 930 : |
|
|
if (siman.getNS() >= siman.getNs()){
|
| 931 : |
|
|
siman.setNS(0);
|
| 932 : |
|
|
siman.incrementNT();
|
| 933 : |
|
|
adjustVm(siman);
|
| 934 : |
|
|
siman.randomizeParams();
|
| 935 : |
|
|
}
|
| 936 : |
|
|
|
| 937 : |
|
|
return ret;
|
| 938 : |
|
|
|
| 939 : |
|
|
}
|
| 940 : |
|
|
|
| 941 : |
|
|
/**
|
| 942 : |
|
|
@brief Decides whether the search must stop.
|
| 943 : |
|
|
It does not take into account the number of iterations, which is already considered by the template
|
| 944 : |
|
|
@param prev old optimum
|
| 945 : |
|
|
@param funcval new/current optimum
|
| 946 : |
|
|
*/
|
| 947 : |
|
|
bool mustTerminate(double prev, double& funcval, Siman &siman, int iters) {
|
| 948 : |
|
|
bool quit = false;
|
| 949 : |
|
|
if (siman.getNT() >= siman.getNt())
|
| 950 : |
|
|
{
|
| 951 : |
|
|
int i;
|
| 952 : |
|
|
DoubleVector fstar = siman.getFstar();
|
| 953 : |
|
|
|
| 954 : |
|
|
siman.setNT(0);
|
| 955 : |
|
|
|
| 956 : |
|
|
for (i = siman.getTempcheck() - 1; i > 0; i--)
|
| 957 : |
|
|
fstar[i] = fstar[i - 1];
|
| 958 : |
|
|
fstar[0] = funcval;
|
| 959 : |
|
|
|
| 960 : |
|
|
if (fabs(prev - funcval) < siman.getSimanneps()) {
|
| 961 : |
|
|
quit = true;
|
| 962 : |
|
|
for (i = 0; i < siman.getTempcheck() - 1; i++)
|
| 963 : |
|
|
if (fabs(fstar[i + 1] - fstar[i]) > siman.getSimanneps())
|
| 964 : |
|
|
quit = false;
|
| 965 : |
|
|
}
|
| 966 : |
|
|
|
| 967 : |
|
|
handle.logMessage(LOGINFO, "Checking convergence criteria after", iters,
|
| 968 : |
|
|
"function evaluations ...");
|
| 969 : |
|
|
|
| 970 : |
|
|
temperature(siman, siman.getX());
|
| 971 : |
|
|
|
| 972 : |
|
|
funcval = prev;
|
| 973 : |
|
|
}
|
| 974 : |
|
|
return quit;
|
| 975 : |
|
|
}
|
| 976 : |
|
|
|
| 977 : |
|
|
void printResult(bool quit, Siman siman, int iters)
|
| 978 : |
|
|
{
|
| 979 : |
|
|
double * score = siman.getScore();
|
| 980 : |
|
|
DoubleVector * bestX = siman.getBestx();
|
| 981 : |
|
|
|
| 982 : |
|
|
handle.logMessage(LOGINFO, "\nStopping Simulated Annealing optimisation algorithm\n");
|
| 983 : |
|
|
handle.logMessage(LOGINFO, "The optimisation stopped after", iters, "function evaluations");
|
| 984 : |
|
|
handle.logMessage(LOGINFO, "The temperature was reduced to", siman.getT());
|
| 985 : |
|
|
if (quit) {
|
| 986 : |
|
|
int* converge = siman.getConverge();
|
| 987 : |
|
|
handle.logMessage(LOGINFO, "The optimisation stopped because an optimum was found for this run");
|
| 988 : |
|
|
|
| 989 : |
|
|
|
| 990 : |
|
|
*converge = 1;
|
| 991 : |
|
|
}
|
| 992 : |
|
|
else {
|
| 993 : |
|
|
handle.logMessage(LOGINFO, "The optimisation stopped because the maximum number of function evaluations");
|
| 994 : |
|
|
handle.logMessage(LOGINFO, "was reached and NOT because an optimum was found for this run");
|
| 995 : |
|
|
}
|
| 996 : |
|
|
handle.logMessage(LOGINFO, "Number of directly accepted points", siman.getNacc());
|
| 997 : |
|
|
handle.logMessage(LOGINFO, "Number of metropolis accepted points", siman.getNaccmet());
|
| 998 : |
|
|
handle.logMessage(LOGINFO, "Number of rejected points", siman.getNrej());
|
| 999 : |
|
|
*score = EcoSystem->SimulateAndUpdate(*bestX);
|
| 1000 : |
|
|
handle.logMessage(LOGINFO, "\nSimulated Annealing finished with a likelihood score of", *score);
|
| 1001 : |
|
|
}
|
| 1002 : |
|
|
};
|
| 1003 : |
|
|
|
| 1004 : |
|
|
// Required
|
| 1005 : |
|
|
std::ostream &operator<<(std::ostream &os, const DoubleVector &p)
|
| 1006 : |
|
|
{
|
| 1007 : |
|
|
os << "";
|
| 1008 : |
|
|
return os;
|
| 1009 : |
|
|
}
|
| 1010 : |
|
|
|
| 1011 : |
|
|
|
| 1012 : |
|
|
void OptInfoSimann::OptimiseLikelihood() {
|
| 1013 : |
|
|
|
| 1014 : |
|
|
//set initial values
|
| 1015 : |
|
|
|
| 1016 : |
|
|
double tmp, p, pp;
|
| 1017 : |
ulcessvp |
12 |
double funcval, trialf;
|
| 1018 : |
ulcessvp |
11 |
int a, i, j, k, l, quit;
|
| 1019 : |
|
|
int rchange, rcheck, rnumber; //Used to randomise the order of the parameters
|
| 1020 : |
|
|
|
| 1021 : |
|
|
handle.logMessage(LOGINFO,
|
| 1022 : |
ulcessvp |
12 |
"\nStarting Simulated Annealing optimisation algorithm\n");
|
| 1023 : |
ulcessvp |
11 |
int nvars = EcoSystem->numOptVariables();
|
| 1024 : |
|
|
DoubleVector x(nvars);
|
| 1025 : |
|
|
DoubleVector init(nvars);
|
| 1026 : |
|
|
DoubleVector trialx(nvars, 0.0);
|
| 1027 : |
|
|
DoubleVector bestx(nvars);
|
| 1028 : |
|
|
DoubleVector scalex(nvars);
|
| 1029 : |
|
|
DoubleVector lowerb(nvars);
|
| 1030 : |
|
|
DoubleVector upperb(nvars);
|
| 1031 : |
|
|
DoubleVector fstar(tempcheck);
|
| 1032 : |
|
|
DoubleVector vm(nvars, vminit);
|
| 1033 : |
|
|
IntVector param(nvars, 0);
|
| 1034 : |
|
|
|
| 1035 : |
|
|
EcoSystem->resetVariables(); //JMB need to reset variables in case they have been scaled
|
| 1036 : |
|
|
if (scale) {
|
| 1037 : |
|
|
EcoSystem->scaleVariables();
|
| 1038 : |
|
|
#ifndef NO_OPENMP
|
| 1039 : |
|
|
int numThr = omp_get_max_threads ( );
|
| 1040 : |
ulcessvp |
14 |
for(i = 0; i < numThr; i++) // scale the variables for the ecosystem of every thread
|
| 1041 : |
ulcessvp |
11 |
EcoSystems[i]->scaleVariables();
|
| 1042 : |
|
|
#endif
|
| 1043 : |
|
|
}
|
| 1044 : |
|
|
EcoSystem->getOptScaledValues(x);
|
| 1045 : |
|
|
EcoSystem->getOptLowerBounds(lowerb);
|
| 1046 : |
|
|
EcoSystem->getOptUpperBounds(upperb);
|
| 1047 : |
|
|
EcoSystem->getOptInitialValues(init);
|
| 1048 : |
|
|
|
| 1049 : |
|
|
for (i = 0; i < nvars; i++) {
|
| 1050 : |
|
|
bestx[i] = x[i];
|
| 1051 : |
|
|
param[i] = i;
|
| 1052 : |
|
|
}
|
| 1053 : |
|
|
|
| 1054 : |
|
|
if (scale) {
|
| 1055 : |
|
|
for (i = 0; i < nvars; i++) {
|
| 1056 : |
|
|
scalex[i] = x[i];
|
| 1057 : |
|
|
// Scaling the bounds, because the parameters are scaled
|
| 1058 : |
|
|
lowerb[i] = lowerb[i] / init[i];
|
| 1059 : |
|
|
upperb[i] = upperb[i] / init[i];
|
| 1060 : |
|
|
if (lowerb[i] > upperb[i]) {
|
| 1061 : |
|
|
tmp = lowerb[i];
|
| 1062 : |
|
|
lowerb[i] = upperb[i];
|
| 1063 : |
|
|
upperb[i] = tmp;
|
| 1064 : |
|
|
}
|
| 1065 : |
|
|
}
|
| 1066 : |
|
|
}
|
| 1067 : |
|
|
|
| 1068 : |
|
|
//funcval is the function value at x
|
| 1069 : |
|
|
funcval = EcoSystem->SimulateAndUpdate(x);
|
| 1070 : |
|
|
if (funcval != funcval) { //check for NaN
|
| 1071 : |
|
|
handle.logMessage(LOGINFO,
|
| 1072 : |
|
|
"Error starting Simulated Annealing optimisation with f(x) = infinity");
|
| 1073 : |
|
|
converge = -1;
|
| 1074 : |
|
|
iters = 1;
|
| 1075 : |
|
|
return;
|
| 1076 : |
|
|
}
|
| 1077 : |
|
|
|
| 1078 : |
|
|
//the function is to be minimised so switch the sign of funcval (and trialf)
|
| 1079 : |
|
|
funcval = -funcval;
|
| 1080 : |
|
|
cs /= lratio; //JMB save processing time
|
| 1081 : |
|
|
for (i = 0; i < tempcheck; i++)
|
| 1082 : |
|
|
fstar[i] = funcval;
|
| 1083 : |
|
|
|
| 1084 : |
|
|
Siman s(seed, seedM, seedP, nvars, nt, ns, param, &x, &lowerb, &upperb, vm, t, rt, (1.0 / ns),
|
| 1085 : |
|
|
tempcheck, simanneps, fstar, lratio, uratio, cs, &bestx, scale, &converge, &score);
|
| 1086 : |
|
|
|
| 1087 : |
|
|
ReproducibleSearch<Siman, DoubleVector, ControlClass, evaluate_f, buildNewParams_f>
|
| 1088 : |
|
|
pa(s, x, simanniter);
|
| 1089 : |
|
|
|
| 1090 : |
|
|
#ifndef NO_OPENMP
|
| 1091 : |
ulcessvp |
12 |
// OpenMP parallelization
|
| 1092 : |
ulcessvp |
11 |
int numThr = omp_get_max_threads ( );
|
| 1093 : |
ulcessvp |
12 |
pa.paral_opt_omp(funcval,numThr,numThr);
|
| 1094 : |
ulcessvp |
11 |
#else
|
| 1095 : |
ulcessvp |
12 |
// sequential code
|
| 1096 : |
|
|
pa.seq_opt(funcval);
|
| 1097 : |
ulcessvp |
11 |
#endif
|
| 1098 : |
|
|
|
| 1099 : |
|
|
}
|
| 1100 : |
|
|
|
| 1101 : |
|
|
|
| 1102 : |
|
|
|
| 1103 : |
|
|
|
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