Dynamic Parameter Adaptation for Meta-Heuristic Optimization Algori...
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Dynamic Parameter Adaptation for Meta-Heuristic Optimization Algorithms Through Type-2 Fuzzy Logic

Oscar Castillo, Patricia Melin, Frumen Olivas, Fevrier Valdez


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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.

About the book
Author
Oscar  Castillo , Patricia  Melin , Frumen  Olivas , Fevrier  Valdez
Imprint
Springer
Collection
SpringerBriefs in Applied Sciences and Technology
Publication date
2018-03-14
Pages
105 pages
Print ISBN
9783319708508
Language
English
Ebook informations
EAN PDF
9783319708515
Price
£44.99
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Copy count 1
Print count 10
File size 2714 Ko
Ce livre est accessible aux handicaps   Informations accessibility
EAN EPUB
9783319708515
Price
£44.99
Read more
Copy count 1
Print count 10
File size 1341 Ko
Ce livre est accessible aux handicaps   Informations accessibility
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