Dynamic Parameter Adaptation for Meta-Heuristic Optimization Algorithms Throu...

(0) Erste Bewertung abgeben
20%
CHF 62.40 Sie sparen CHF 15.60
Print on Demand - Exemplar wird für Sie gedruckt.
Kartonierter Einband
Kein Rückgaberecht!

Beschreibung

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.

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



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

Mehr anzeigen

Produktinformationen

Titel
Dynamic Parameter Adaptation for Meta-Heuristic Optimization Algorithms Through Type-2 Fuzzy Logic
Autor
EAN
9783319708508
ISBN
978-3-319-70850-8
Format
Kartonierter Einband
Herausgeber
Springer Nature EN
Genre
Allgemeines & Lexika
Anzahl Seiten
105
Gewicht
1883g
Größe
H235mm x B155mm
Jahr
2018
Untertitel
Englisch
Auflage
1st ed. 2018
Mehr anzeigen
Andere Kunden kauften auch