Differential Evolution: A Handbook for Global Permutation-Based Combinatorial Optimization / Edition 1

by Godfrey C. Onwubolu, Donald Davendra

Overview

What is combinatorial optimization? Traditionally, a problem is considered to be c- binatorial if its set of feasible solutions is bothfinite 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 problem’s feasible solutions - permutations of city labels - c- prise afinite, discrete set. By contrast, Differential Evolution was originally designed to optimize functions defined 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 classifled as combinatorial because their feasible sets are discrete. For example, the knapsack problem’s goal is to pack objects of differing weight and value so that the knapsack’s 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.

Product Details

ISBN-13:	9783540921509
Publisher:	Springer Berlin Heidelberg
Publication date:	02/13/2009
Series:	Studies in Computational Intelligence , #175
Edition description:	2009
Pages:	213
Product dimensions:	6.30(w) x 9.40(h) x 0.80(d)

Table of Contents

Motivation for Differential Evolution for Permutative—Based Combinatorial Problems.- Differential Evolution for Permutation—Based Combinatorial Problems.- Forward Backward Transformation.- Relative Position Indexing Approach.- Smallest Position Value Approach.- Discrete/Binary Approach.- Discrete Set Handling.