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2018 | OriginalPaper | Buchkapitel

Generalized Self-adapting Particle Swarm Optimization Algorithm

verfasst von : Mateusz Uliński, Adam Żychowski, Michał Okulewicz, Mateusz Zaborski, Hubert Kordulewski

Erschienen in: Parallel Problem Solving from Nature – PPSN XV

Verlag: Springer International Publishing

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Abstract

This paper presents a generalized view on the family of swarm optimization algorithms. Paper focuses on a few distinct variants of the Particle Swarm Optimization and also incorporates one type of Differential Evolution algorithm as a particle’s behavior. Each particle type is treated as an agent enclosed in a framework imposed by a basic PSO. Those agents vary on the velocity update procedure and utilized neighborhood. This way, a hybrid swarm optimization algorithm, consisting of a heterogeneous set of particles, is formed. That set of various optimization agents is governed by an adaptation scheme, which is based on the roulette selection used in evolutionary approaches. The proposed Generalized Self-Adapting Particle Swarm Optimization algorithm performance is assessed a well-established BBOB benchmark set and proves to be better than any of the algorithms its incorporating.

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Vorheriges Kapitel Design of a Surrogate Model Assisted (1 + 1)-ES

Nächstes Kapitel PSO-Based Search Rules for Aerial Swarms Against Unexplored Vector Fields via Genetic Programming

https://bitbucket.org/pl-edu-pw-mini-optimization/corpoalgorithm.

http://coco.gforge.inria.fr/.

Detailed outcomes are available at http://pages.mini.pw.edu.pl/~zychowskia/gapso.

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Titel: Generalized Self-adapting Particle Swarm Optimization Algorithm
verfasst von: Mateusz Uliński
Adam Żychowski
Michał Okulewicz
Mateusz Zaborski
Hubert Kordulewski
Verlag: Springer International Publishing
Buch: Parallel Problem Solving from Nature – PPSN XV
Print ISBN: 978-3-319-99252-5

Electronic ISBN: 978-3-319-99253-2

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-319-99253-2_3

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