Dynamic Parameter Adaptation for Meta-Heuristic Optimization Algorithms Through Type-2 Fuzzy Logic (SpringerBriefs in Computational Intelligence)

Olivas, Frumen; Valdez, Fevrier; Castillo, Oscar; Melin, Patricia

ISBN 10: 3319708503 / ISBN 13: 9783319708508

Published by Springer, 2018

In. Seller Inventory # ria9783319708508_new

Synopsis:

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.

From the Back Cover:

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.

Bibliographic Details

Title: Dynamic Parameter Adaptation for ...
Publisher: Springer
Publication Date: 2018
Binding: Soft cover
Condition: New