
Dynamic Parameter Adaptation for Meta-Heuristic Optimization Algorithms Through Type-2 Fuzzy Logic
Springer (Publisher)
Published on 22. March 2018
Book
Paperback/Softback
VII, 105 pages
978-3-319-70850-8 (ISBN)
Description
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.
More details
Series
Edition
2018 ed.
Language
English
Place of publication
Cham
Switzerland
Publishing group
Springer International Publishing
Target group
Professional and scholarly
Illustrations
25 s/w Abbildungen
VII, 105 p. 25 illus.
Dimensions
Height: 23.5 cm
Width: 15.5 cm
Weight
1883 gr
ISBN-13
978-3-319-70850-8 (9783319708508)
DOI
10.1007/978-3-319-70851-5
Schweitzer Classification
Other editions
Additional editions

Frumen Olivas | Fevrier Valdez | Oscar Castillo
Dynamic Parameter Adaptation for Meta-Heuristic Optimization Algorithms Through Type-2 Fuzzy Logic
E-Book
03/2018
1st Edition
Springer
€53.49
Available for download
Content
Introduction.- Theory and Background.- Problems Statement.- Methodology.- Simulation Results.- Statistical Analysis and Comparison of Results.