Global Optimization Toolbox User's Guide - MathWorks

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5 Using the Genetic Algorithm 5-76 Select Distance in the Plot functions pane. Set Initial range in the Population pane of the Options pane to [1;1.1]. Click Start in Run solver and view results. Although the results of genetic algorithm computations are random, your results are similar to the following figure, with a best fitness function value of approximately 2. The upper plot, which displays the best fitness at each generation, shows little progress in lowering the fitness value. The lower plot shows the average distance between individuals at each generation, which is a good measure of the diversity of a population. For this setting of initial range, there is too little diversity for the algorithm to make progress.
Genetic Algorithm Examples Next, try setting Initial range to [1;100] and running the algorithm. This time the results are more variable. You might obtain a plot with a best fitness value of 35, as in the following plot. You might obtain different results. This time, the genetic algorithm makes progress, but because the average distance between individuals is so large, the best individuals are far from the optimal solution. Finally, set Initial range to [1;2] and run the genetic algorithm. Again, there is variability in the result, but you might obtain a result similar to the following figure. Run the optimization several times, and you eventually obtain a final point near [0;0], with a fitness function value near 0. 5-77
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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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Page 274 and 275: 5 Using the Genetic Algorithm 5-26
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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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