Global Optimization Toolbox User's Guide - MathWorks

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3 Using GlobalSearch and MultiStart 3-62 output = funcCount: 32760 localSolverTotal: 200 localSolverSuccess: 51 localSolverIncomplete: 149 localSolverNoSolution: 0 message: [1x143 char] manymins = 1x51 GlobalOptimSolution Properties: X Fval Exitflag Output X0 This time MultiStart found the global minimum, and found 53 total local minima. To see the range of local solutions, enter hist([manymins.Fval]).
Tighter Bound on the Start Points. Suppose you believe that the interesting local solutions have absolute values of all components less than 100. The default value of the bound on start points is 1000. To use a different value of the bound, generate a RandomStartPointSet with the ArtificialBound property set to 100: startpts = RandomStartPointSet('ArtificialBound',100,... 'NumStartPoints',50); [x fval eflag output manymins] = run(ms,problem,startpts) MultiStart completed some of the runs from the start points. 27 out of 50 local solver runs converged with a positive local solver exit flag. x = 1.0e-08 * 0.9725 -0.6198 fval = 1.4955e-15 eflag = 2 output = funcCount: 7482 localSolverTotal: 50 localSolverSuccess: 27 localSolverIncomplete: 23 localSolverNoSolution: 0 message: [1x142 char] manymins = 1x22 GlobalOptimSolution Properties: X Fval Exitflag Improve Results 3-63
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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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Page 64 and 65: 3 Using GlobalSearch and MultiStart
Page 150 and 151: 4 Using Direct Search 4-2 What Is D
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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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9 Options Reference 9-66
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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)
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gamultiobj 12-24 See Also ga | gaop
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gaoptimset 12-26 Purpose Create gen
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gaoptimset 12-28 Option Description
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GlobalOptimSolution 12-34 Copy Sema
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GlobalSearch 12-36 'off' —Nodispl
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GlobalSearch 12-38 Default: [] Star
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CustomStartPointSet.list 12-40 Purp
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RandomStartPointSet.list 12-42 Algo
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MultiStart 12-44 OutputFcns A funct
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MultiStart 12-46 Methods Copy Seman
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patternsearch 12-48 Ax ≤ b Aeq
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patternsearch 12-50 Exit Flag 1 Mea
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patternsearch 12-52 x = 0.6667 1.33
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psoptimget 12-54 Purpose Obtain val
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psoptimset 12-56 Option Description
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psoptimset 12-58 Option Description
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GlobalSearch.run 12-60 Purpose Find
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GlobalSearch.run 12-62 funcCount Nu
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MultiStart.run 12-64 Purpose Run lo
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MultiStart.run 12-66 output 2 At le
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RandomStartPointSet 12-68 Purpose R
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saoptimget 12-70 Purpose Values of
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saoptimset 12-72 AnnealingFcn: @ann
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saoptimset 12-74 Option Description
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simulannealbnd 12-76 solver 'simula
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simulannealbnd 12-78 x = 0.0392 -31
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simulannealbnd 12-80 See Also ga |
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A Examples A-2 GlobalSearch and Mul
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A Examples A-4 Genetic Algorithm
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A Examples A-6
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Index global 5-97 local 5-97 minimi
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Global Optimization Toolbox User's Guide - MathWorks

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