Global Optimization Toolbox User's Guide - MathWorks

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saoptimset 12-74 Option Description Values PlotInterval Plot functions are called at every interval Positive integer |{1} ReannealInterval Reannealing interval Positive integer | {100} StallIterLimit Number of iterations over which average change in fitness function value at current point is less than options.TolFun TemperatureFcn Function used to update temperature schedule TimeLimit The algorithm stops after running for TimeLimit seconds TolFun Termination tolerance on function value See Also saoptimget | simulannealbnd Positive integer | {500*numberOfVariables} Function handle | @temperatureboltz | @temperaturefast | {@temperatureexp} How To • “Simulated Annealing Options” on page 9-56 Positive scalar | {Inf} Positive scalar | {1e-6}
simulannealbnd Purpose Find minimum of function using simulated annealing algorithm Syntax x = simulannealbnd(fun,x0) x = simulannealbnd(fun,x0,lb,ub) x = simulannealbnd(fun,x0,lb,ub,options) x = simulannealbnd(problem) [x,fval] = simulannealbnd(...) [x,fval,exitflag] = simulannealbnd(...) [x,fval,exitflag,output] = simulannealbnd(fun,...) Description x = simulannealbnd(fun,x0) starts at x0 and finds a local minimum x to the objective function specified by the function handle fun. The objective function accepts input x and returns a scalar function value evaluated at x. x0 may be a scalar or a vector. x = simulannealbnd(fun,x0,lb,ub) defines a set of lower and upper bounds on the design variables, x, so that a solution is found in the range lb ≤ x ≤ ub. Use empty matrices for lb and ub if no bounds exist. Set lb(i) to -Inf if x(i) is unbounded below; set ub(i) to Inf if x(i) is unbounded above. x = simulannealbnd(fun,x0,lb,ub,options) minimizes with the default optimization parameters replaced by values in the structure options, which can be created using the saoptimset function. See the saoptimset reference page for details. x = simulannealbnd(problem) finds the minimum for problem, where problem is a structure containing the following fields: objective Objective function x0 Initial point of the search lb Lower bound on x ub Upper bound on x rngstate Optional field to reset the state of the random number generator 12-75
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Global Optimization Toolbox User’
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Revision History January 2004 Onlin
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vi Contents 3 How to Write a Functi
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viii Contents An Unsuccessful Poll
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x Contents 6 Genetic Algorithm Usin
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xii Contents 8 9 Bibliography .....
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11 12 A Getting Started xiv Content
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1 Introducing Global Optimization T
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2 Write Files for Optimization Func
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3 Using GlobalSearch and MultiStart
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4 Using Direct Search 4-2 What Is D
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4 Using Direct Search 4-4 Choose so
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4 Using Direct Search 4-6 Optimize
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4 Using Direct Search 4-8 The reaso
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4 Using Direct Search 4-10 Pattern
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4 Using Direct Search 4-12 The algo
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4 Using Direct Search 4-14 The prob
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4 Using Direct Search 4-16 Note By
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4 Using Direct Search 4-18 8*[0 -1]
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4 Using Direct Search 4-20 2 1.5 1
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4 Using Direct Search 4-22 The dist
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4 Using Direct Search 4-24 Update c
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4 Using Direct Search 4-26 Search T
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4 Using Direct Search 4-28 3 Set th
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4 Using Direct Search 4-30 Referenc
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4 Using Direct Search 4-32 Pattern
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4 Using Direct Search 4-34 - Set Ae
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4 Using Direct Search 4-36 The uppe
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4 Using Direct Search 4-38 if optim
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4 Using Direct Search 4-40 Now, whe
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4 Using Direct Search 4-42 Custom P
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4 Using Direct Search 4-44 x = -4.7
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4 Using Direct Search 4-46 MaxFunEv
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4 Using Direct Search 4-48 You can
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4 Using Direct Search 4-50 [-100...
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4 Using Direct Search 4-52 0 −5
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4 Using Direct Search 4-54 mesh and
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4 Using Direct Search 4-56 Example:
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4 Using Direct Search 4-58 2 Choose
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4 Using Direct Search 4-60 Algorith
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4 Using Direct Search 4-62 The most
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4 Using Direct Search 4-64 To see t
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4 Using Direct Search 4-66 With the
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4 Using Direct Search 4-68 3 Run th
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4 Using Direct Search 4-70 The firs
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4 Using Direct Search 4-72 When you
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4 Using Direct Search 4-74 Note For
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4 Using Direct Search 4-76 Iter f-c
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4 Using Direct Search 4-78 3 Run th
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4 Using Direct Search 4-80 This tim
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4 Using Direct Search 4-82 The patt
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4 Using Direct Search 4-84 Best Obj
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4 Using Direct Search 4-86 If x0 is
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4 Using Direct Search 4-88 The same
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4 Using Direct Search 4-90 The init
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4 Using Direct Search 4-92 ylabel('
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4 Using Direct Search 4-94 distance
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4 Using Direct Search 4-96 if nargi
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4 Using Direct Search 4-98 You can
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4 Using Direct Search 4-100 outpt =
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5 Using the Genetic Algorithm 5-2 W
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5 Using the Genetic Algorithm 5-4 U
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5 Using the Genetic Algorithm 5-6 T
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5 Using the Genetic Algorithm 5-8 G
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5 Using the Genetic Algorithm 5-10
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6 Using Simulated Annealing 6-2 Wha
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6 Using Simulated Annealing 6-4 Usi
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6 Using Simulated Annealing 6-6 To
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6 Using Simulated Annealing 6-8 Man
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6 Using Simulated Annealing 6-10 Si
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6 Using Simulated Annealing 6-12 Ho
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6 Using Simulated Annealing 6-14 T
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6 Using Simulated Annealing 6-16 Us
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6 Using Simulated Annealing 6-18 op
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6 Using Simulated Annealing 6-20 x,
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6 Using Simulated Annealing 6-22
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7 Multiobjective Optimization 7-2 W
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7 Multiobjective Optimization 7-4 F
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7 Multiobjective Optimization 7-6 U
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7 Multiobjective Optimization 7-8 3
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7 Multiobjective Optimization 7-10
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8 Parallel Processing 8-2 Backgroun
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8 Parallel Processing 8-4 Suppose,
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8 Parallel Processing 8-6 reaches a
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8 Parallel Processing 8-8 Implement
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8 Parallel Processing 8-10 Run gamu
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8 Parallel Processing 8-12 MultiSta
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8 Parallel Processing 8-14 After yo
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8 Parallel Processing 8-16 Parallel
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8 Parallel Processing 8-18
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9 Options Reference 9-2 GlobalSearc
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9 Options Reference 9-4 run when th
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9 Options Reference 9-6 If you spec
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9 Options Reference 9-8 If MaxWaitC
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9 Options Reference 9-10 In this se
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9 Options Reference 9-12 - TolX —
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9 Options Reference 9-14 the number
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9 Options Reference 9-16 Note If yo
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9 Options Reference 9-18 The search
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9 Options Reference 9-20 Off or On
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9 Options Reference 9-22 Specify
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9 Options Reference 9-24 - TolFun
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9 Options Reference 9-26 When Evalu
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9 Options Reference 9-28 Option Ava
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9 Options Reference 9-30 Option Ava
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9 Options Reference 9-32 See “Set
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9 Options Reference 9-34 To display
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9 Options Reference 9-36 Selection
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9 Options Reference 9-38 The functi
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9 Options Reference 9-40 - Set Scal
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9 Options Reference 9-42 The input
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9 Options Reference 9-44 options =
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9 Options Reference 9-46 - Selects
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9 Options Reference 9-48 Your cross
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9 Options Reference 9-50 space (gen
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9 Options Reference 9-52 Enter @myf
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9 Options Reference 9-54 Max Constr
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9 Options Reference 9-56 Simulated
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9 Options Reference 9-58 optimvalue
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9 Options Reference 9-60 - @anneali
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9 Options Reference 9-62 Stopping C
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9 Options Reference 9-64 - 'iter'
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10 Function Reference 10-2 GlobalSe
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10 Function Reference 10-4 Genetic
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10 Function Reference 10-6 Simulate
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11 Class Reference 11-2
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createOptimProblem 12-2 Purpose Cre
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createOptimProblem 12-4 Output Argu
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CustomStartPointSet 12-6 Get the fo
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ga 12-8 x = ga(fitnessfcn,nvars,A,b
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ga 12-10 If the problem has m linea
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ga 12-12 where constraintfile.m is
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ga 12-14 Exit Flag Meaning 1 Withou
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ga 12-16 Examples Given the followi
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gamultiobj 12-18 Purpose Find minim
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gamultiobj 12-20 rngstate Optional
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gamultiobj 12-22 end y(i,1) = x(i)
Page 516 and 517: gamultiobj 12-24 See Also ga | gaop
Page 518 and 519: gaoptimset 12-26 Purpose Create gen
Page 520 and 521: gaoptimset 12-28 Option Description
Page 526 and 527: GlobalOptimSolution 12-34 Copy Sema
Page 528 and 529: GlobalSearch 12-36 'off' —Nodispl
Page 530 and 531: GlobalSearch 12-38 Default: [] Star
Page 532 and 533: CustomStartPointSet.list 12-40 Purp
Page 534 and 535: RandomStartPointSet.list 12-42 Algo
Page 536 and 537: MultiStart 12-44 OutputFcns A funct
Page 538 and 539: MultiStart 12-46 Methods Copy Seman
Page 540 and 541: patternsearch 12-48 Ax ≤ b Aeq
Page 542 and 543: patternsearch 12-50 Exit Flag 1 Mea
Page 544 and 545: patternsearch 12-52 x = 0.6667 1.33
Page 546 and 547: psoptimget 12-54 Purpose Obtain val
Page 548 and 549: psoptimset 12-56 Option Description
Page 550 and 551: psoptimset 12-58 Option Description
Page 552 and 553: GlobalSearch.run 12-60 Purpose Find
Page 554 and 555: GlobalSearch.run 12-62 funcCount Nu
Page 556 and 557: MultiStart.run 12-64 Purpose Run lo
Page 558 and 559: MultiStart.run 12-66 output 2 At le
Page 560 and 561: RandomStartPointSet 12-68 Purpose R
Page 562 and 563: saoptimget 12-70 Purpose Values of
Page 564 and 565: saoptimset 12-72 AnnealingFcn: @ann
Page 568 and 569: simulannealbnd 12-76 solver 'simula
Page 570 and 571: simulannealbnd 12-78 x = 0.0392 -31
Page 572 and 573: simulannealbnd 12-80 See Also ga |
Page 574 and 575: A Examples A-2 GlobalSearch and Mul
Page 576 and 577: A Examples A-4 Genetic Algorithm
Page 578 and 579: A Examples A-6
Page 580 and 581: Index global 5-97 local 5-97 minimi
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Global Optimization Toolbox User's Guide - MathWorks

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