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Dynamic parameter adaptation for meta-heuristic optimization algorithms through type-2 fuzzy logic

Author: Frumen Olivas; Fevrier Valdez; Oscar Castillo; Patricia Melin
Publisher: Cham, Switzerland : Springer, [2018]
Series: SpringerBriefs in applied sciences and technology.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
In this book, a methodology for parameter adaptation in meta-heuristic op-timization methods is proposed. This methodology is based on using met-rics about the population of the meta-heuristic methods, to decide through a fuzzy inference system the best parameter values that were carefully se-lected to be adjusted. With this modification of parameters we want to find a better model of the behavior of the  Read more...
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Genre/Form: Electronic books
Additional Physical Format: Printed edition:
(OCoLC)1038299539
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Frumen Olivas; Fevrier Valdez; Oscar Castillo; Patricia Melin
ISBN: 9783319708515 3319708511
OCLC Number: 1029091355
Description: 1 online resource (VII, 105 pages) : illustrations
Contents: Introduction --
Theory and Background --
Problems Statement --
Methodology --
Simulation Results --
Statistical Analysis and Comparison of Results.
Series Title: SpringerBriefs in applied sciences and technology.
Responsibility: by Frumen Olivas, Fevrier Valdez, Oscar Castillo, Patricia Melin.

Abstract:

In this book, a methodology for parameter adaptation in meta-heuristic op-timization methods is proposed. This methodology is based on using met-rics about the population of the meta-heuristic methods, to decide through a fuzzy inference system the best parameter values that were carefully se-lected to be adjusted. With this modification of parameters we want to find a better model of the behavior of the optimization method, because with the modification of parameters, these will affect directly the way in which the global or local search are performed. Three different optimization methods were used to verify the improve-ment of the proposed methodology. In this case the optimization methods are: PSO (Particle Swarm Optimization), ACO (Ant Colony Optimization) and GSA (Gravitational Search Algorithm), where some parameters are se-lected to be dynamically adjusted, and these parameters have the most im-pact in the behavior of each optimization method. Simulation results show that the proposed methodology helps to each optimization method in obtaining better results than the results obtained by the original method without parameter adjustment.

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