Differential Evolution: A Handbook for Global Permutation-Based Combinatorial Optimization
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Format
Mixed media product
Språk
Engelska
Antal sidor
213
Utgivningsdatum
2009-01-13
Upplaga
2009 ed.
Förlag
Springer-Verlag Berlin and Heidelberg GmbH & Co. K
Medarbetare
Onwubolu, Godfrey C. (ed.), Davendra, Donald (ed.)
Illustratör/Fotograf
115 schwarz-weiße Tabellen 105 schwarz-weiße Abbildungen Bibliographie
Illustrationer
XVII, 213 p. With CD-ROM.
Dimensioner
239 x 160 x 20 mm
Vikt
477 g
Antal komponenter
1
Komponenter
1 Item
ISBN
9783540921509

Differential Evolution: A Handbook for Global Permutation-Based Combinatorial Optimization

av Godfrey C Onwubolu, Donald Davendra
Mixed media product,  Engelska, 2009-01-13
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What is combinatorial optimization? Traditionally, a problem is considered to be c- binatorial if its set of feasible solutions is both ?nite and discrete, i. e. , enumerable. For example, the traveling salesman problem asks in what order a salesman should visit the cities in his territory if he wants to minimize his total mileage (see Sect. 2. 2. 2). The traveling salesman problems feasible solutions - permutations of city labels - c- prise a ?nite, discrete set. By contrast, Differential Evolution was originally designed to optimize functions de?ned on real spaces. Unlike combinatorial problems, the set of feasible solutions for real parameter optimization is continuous. Although Differential Evolution operates internally with ?oating-point precision, it has been applied with success to many numerical optimization problems that have t- ditionally been classi?ed as combinatorial because their feasible sets are discrete. For example, the knapsack problems goal is to pack objects of differing weight and value so that the knapsacks total weight is less than a given maximum and the value of the items inside is maximized (see Sect. 2. 2. 1). The set of feasible solutions - vectors whose components are nonnegative integers - is both numerical and discrete. To handle such problems while retaining full precision, Differential Evolution copies ?oating-point - lutions to a temporary vector that, prior to being evaluated, is truncated to the nearest feasible solution, e. g. , by rounding the temporary parameters to the nearest nonnegative integer.
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Godfrey C Onwubolu, Donald Davendra

Innehållsförteckning

Motivation for Differential Evolution for PermutativeBased Combinatorial Problems.- Differential Evolution for PermutationBased Combinatorial Problems.- Forward Backward Transformation.- Relative Position Indexing Approach.- Smallest Position Value Approach.- Discrete/Binary Approach.- Discrete Set Handling.

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