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

  • Frumen Olivas
  • Fevrier Valdez
  • Oscar Castillo
  • Patricia Melin

Part of the SpringerBriefs in Applied Sciences and Technology book series (BRIEFSAPPLSCIENCES)

Also part of the SpringerBriefs in Computational Intelligence book sub series (BRIEFSINTELL)

Table of contents

  1. Front Matter
    Pages i-vii
  2. Frumen Olivas, Fevrier Valdez, Oscar Castillo, Patricia Melin
    Pages 1-1
  3. Frumen Olivas, Fevrier Valdez, Oscar Castillo, Patricia Melin
    Pages 3-10
  4. Frumen Olivas, Fevrier Valdez, Oscar Castillo, Patricia Melin
    Pages 11-21
  5. Frumen Olivas, Fevrier Valdez, Oscar Castillo, Patricia Melin
    Pages 23-31
  6. Frumen Olivas, Fevrier Valdez, Oscar Castillo, Patricia Melin
    Pages 33-46
  7. Frumen Olivas, Fevrier Valdez, Oscar Castillo, Patricia Melin
    Pages 47-50
  8. Frumen Olivas, Fevrier Valdez, Oscar Castillo, Patricia Melin
    Pages 51-52
  9. Back Matter
    Pages 53-105

About this book

Introduction

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.

Keywords

Computational Intelligence Intelligent Systems Fuzzy Systems Hybrid Systems Type 2 Fuzzy Logic

Authors and affiliations

  • Frumen Olivas
    • 1
  • Fevrier Valdez
    • 2
  • Oscar Castillo
    • 3
  • Patricia Melin
    • 4
  1. 1.Division of Graduate StudiesTijuana Institute of TechnologyTijuanaMexico
  2. 2.Division of Graduate StudiesTijuana Institute of TechnologyTijuanaMexico
  3. 3.Division of Graduate StudiesTijuana Institute of TechnologyTijuanaMexico
  4. 4.Division of Graduate StudiesTijuana Institute of TechnologyTijuanaMexico

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-70851-5
  • Copyright Information The Author(s) 2018
  • Publisher Name Springer, Cham
  • eBook Packages Engineering
  • Print ISBN 978-3-319-70850-8
  • Online ISBN 978-3-319-70851-5
  • Series Print ISSN 2191-530X
  • Series Online ISSN 2191-5318
  • Buy this book on publisher's site