New PDF release: Metaheuristics: Progress in Complex Systems Optimization

By Karl F. Doerner, Michel Gendreau, Peter Greistorfer, Walter Gutjahr, Richard F. Hartl, Marc Reimann

ISBN-10: 0387719199

ISBN-13: 9780387719191

This book’s objective is to supply a number of other kinds of knowledge: a delineation of normal metaheuristics tools, a couple of cutting-edge articles from a number of famous classical program components in addition to an outlook to fashionable computational tools in promising new components. accordingly, this e-book may possibly both function a textbook in graduate classes for college students, as a reference ebook for individuals drawn to engineering or social sciences, and as a suite of recent and promising avenues for researchers operating during this field.

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Additional resources for Metaheuristics: Progress in Complex Systems Optimization (Operations Research Computer Science Interfaces Series)

Sample text

All subsets of size two are generated from the reference set, with the restriction that at least one of the solutions in each subset was included in the reference set during the previous iteration. 5. The solutions are combined using deterministic formulas, yielding five different new solutions for every pair of reference set solutions combined. x DET SS – combining the deterministic components with best test results in Section 4. 1. Using the strategic diversification generator, with the only randomization appearing on the rare occasion that the generator needs a new seed.

M}, and n columns in N = {1, . . , n}. A mathematical formulation for SCP is (SCP) : min {z = cx : Ax ≥ 1, x ∈ Bn } , where c ∈ Zn+ and A is a matrix of 0’s and 1’s. In the following, we call cover a binary vector x ∈ Bn that is a feasible solution of SCP, while a prime cover is a cover with no redundant columns. Also, let Ji = {j ∈ N : aij = 1} be the index set of columns covering row i, and Ij = {i ∈ M : aij = 1} the index set of rows covered by column j. Many real-world applications can be formulated as SCP, including traditional delivery and routing problems, as well as scheduling and location problems.

1. x GA SS – based on concepts from Genetic Algorithms 1. and 2. same as for SB SS. 3. In this approach the reference set is rebuilt using trial solutions only (cf. the generational approach). There is no need for the third tier, so we use b1=20, b2=5, and b3=0. 4. All subsets of size two is generated at each iteration, yielding 300 different subsets when using the specified reference set size of 25. 5. Solutions are combined using the cross-over based method, with ncp=5. x D2/3S SS – based on combining sets of both two and three solutions 1.

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