Global Optimization Toolbox User's Guide - MathWorks

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8 Parallel Processing 8-2 Background In this section... “Parallel Processing Types in Global Optimization Toolbox” on page 8-2 “How Toolbox Functions Distribute Processes” on page 8-3 Parallel Processing Types in Global Optimization Toolbox Parallel processing is an attractive way to speed optimization algorithms. To use parallel processing, you must have a Parallel Computing Toolbox license, and have a MATLAB worker pool open (matlabpool). For more information, see“HowtoUseParallelProcessing”onpage8-11. Global Optimization Toolbox solvers use parallel computing in various ways. Solver Parallel? Parallel Characteristics GlobalSearch × No parallel functionality. However, fmincon can use parallel gradient estimation when run in GlobalSearch. See “Using Parallel Computing in Optimization Toolbox” in the Optimization Toolbox documentation. MultiStart ga, gamultiobj Start points distributed to multiple processors. From these points, local solvers run to completion. For more details, see “MultiStart” on page 8-5 and “How to Use Parallel Processing” on page 8-11. For fmincon, no parallel gradient estimation with parallel MultiStart. Population evaluated in parallel, which occurs once per iteration. For more details, see “Genetic Algorithm” on page 8-8 and “How to Use Parallel Processing” on page 8-11. No vectorization of fitness or constraint functions.
Solver Parallel? Parallel Characteristics patternsearch Poll points evaluated in parallel, which occurs once per iteration. For more details, see “Pattern Search” on page 8-7 and “How to Use Parallel Processing” on page 8-11. No vectorization of fitness or constraint functions. simulannealbnd × No parallel functionality. However, simulannealbnd can use a hybrid function that runs in parallel. See “Simulated Annealing” on page 8-10. In addition, several solvers have hybrid functions that run after they finish. Some hybrid functions can run in parallel. Also, most patternsearch search methods can run in parallel. For more information, see “Parallel Search Functions or Hybrid Functions” on page 8-14. How Toolbox Functions Distribute Processes “parfor Characteristics and Caveats” on page 8-3 “MultiStart” on page 8-5 “GlobalSearch” on page 8-6 “Pattern Search” on page 8-7 “Genetic Algorithm” on page 8-8 “Parallel Computing with gamultiobj” on page 8-9 “Simulated Annealing” on page 8-10 parfor Characteristics and Caveats No Nested parfor Loops. Solvers employ the Parallel Computing Toolbox parfor function to perform parallel computations. Note parfor doesnotworkinparallelwhencalledfromwithinanother parfor loop. Background 8-3
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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 352 and 353: 5 Using the Genetic Algorithm 5-104
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Page 388 and 389: 7 Multiobjective Optimization 7-2 W
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Page 420 and 421: 9 Options Reference 9-2 GlobalSearc
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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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