Authors:
- Proposes a methodology for parameter adaptation in meta-heuristic optimization methods
- Uses three different optimization methods: PSO (Particle Swarm Optimization), ACO (Ant Colony Optimization) and GSA (Gravitational Search Algorithm), to verify the improvement of the proposed methodology
- Demonstrates the advantage of the methodology by using various simulations
Part of the book series: SpringerBriefs in Applied Sciences and Technology (BRIEFSAPPLSCIENCES)
Part of the book sub series: SpringerBriefs in Computational Intelligence (BRIEFSINTELL)
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Table of contents (7 chapters)
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Front Matter
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Back Matter
About this book
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.
Authors and Affiliations
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Division of Graduate Studies, Tijuana Institute of Technology, Tijuana, Mexico
Frumen Olivas, Fevrier Valdez, Oscar Castillo, Patricia Melin
Bibliographic Information
Book Title: Dynamic Parameter Adaptation for Meta-Heuristic Optimization Algorithms Through Type-2 Fuzzy Logic
Authors: Frumen Olivas, Fevrier Valdez, Oscar Castillo, Patricia Melin
Series Title: SpringerBriefs in Applied Sciences and Technology
DOI: https://doi.org/10.1007/978-3-319-70851-5
Publisher: Springer Cham
eBook Packages: Engineering, Engineering (R0)
Copyright Information: The Author(s) 2018
Softcover ISBN: 978-3-319-70850-8Published: 22 March 2018
eBook ISBN: 978-3-319-70851-5Published: 14 March 2018
Series ISSN: 2191-530X
Series E-ISSN: 2191-5318
Edition Number: 1
Number of Pages: VII, 105
Number of Illustrations: 25 b/w illustrations