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Dynamic parameter adaptation for met...

Olivas, Frumen.

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Dynamic parameter adaptation for meta-heuristic optimization algorithms through type-2 fuzzy logic

Record Type:	Electronic resources : Monograph/item
Title/Author:	Dynamic parameter adaptation for meta-heuristic optimization algorithms through type-2 fuzzy logic/ by Frumen Olivas ... [et al.].
other author:	Olivas, Frumen.
Published:	Cham :Springer International Publishing : : 2018.,
Description:	vii, 105 p. :ill., digital ;24 cm.
[NT 15003449]:	Introduction -- Theory and Background -- Problems Statement -- Methodology -- Simulation Results -- Statistical Analysis and Comparison of Results.
Contained By:	Springer eBooks
Subject:	Engineering. -
Online resource:	http://dx.doi.org/10.1007/978-3-319-70851-5
ISBN:	9783319708515

Dynamic parameter adaptation for meta-heuristic optimization algorithms through type-2 fuzzy logic
Dynamic parameter adaptation for meta-heuristic optimization algorithms through type-2 fuzzy logic[electronic resource] /by Frumen Olivas ... [et al.]. - Cham :Springer International Publishing :2018. - vii, 105 p. :ill., digital ;24 cm. - SpringerBriefs in applied sciences and technology,2191-530X. - SpringerBriefs in applied sciences and technology..

Introduction -- Theory and Background -- Problems Statement -- Methodology -- Simulation Results -- Statistical Analysis and Comparison of Results.

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.

ISBN: 9783319708515

Standard No.: 10.1007/978-3-319-70851-5doiSubjects--Topical Terms:

586835
Engineering.

LC Class. No.: Q342

Dewey Class. No.: 006.3

Dynamic parameter adaptation for meta-heuristic optimization algorithms through type-2 fuzzy logic
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245 0 0 $a Dynamic parameter adaptation for meta-heuristic optimization algorithms through type-2 fuzzy logic $h [electronic resource] / $c by Frumen Olivas ... [et al.].
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505 0 $a Introduction -- Theory and Background -- Problems Statement -- Methodology -- Simulation Results -- Statistical Analysis and Comparison of Results.
520 $a 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.
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