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Erschienen in:
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2015 | OriginalPaper | Buchkapitel

Particle Swarm Optimization Algorithm for Dynamic Environments

verfasst von : Sadrollah Sadeghi, Hamid Parvin, Farhad Rad

Erschienen in: Advances in Artificial Intelligence and Soft Computing

Verlag: Springer International Publishing

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Abstract

Particle Swarm Optimization (PSO) algorithm is considered as one of the crowd intelligence optimization algorithms. Dynamic optimization problems in which change(s) may happen over the time are harder to manage than static optimization problems. In this paper an algorithm based on PSO and memory for solving dynamic optimization problems has been proposed. The proposed algorithm uses the memory to store the aging best solutions and uses partitioning for preventing convergence in the population. The proposed approach has been tested on moving peaks benchmark (MPB). The MPB is a suitable problem for simulating dynamic optimization problems. The experimental results on the moving peaks benchmark show that the proposed algorithm is superior to several other well-known and state-of-the-art algorithms in dynamic environments.

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Vorheriges Kapitel A System for Political Districting in the State of Mexico
Nächstes Kapitel A Migrating Birds Optimization Algorithm for Machine-Part Cell Formation Problems
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Metadaten
Titel
Particle Swarm Optimization Algorithm for Dynamic Environments
verfasst von
Sadrollah Sadeghi
Hamid Parvin
Farhad Rad
Copyright-Jahr
2015
Buch
Advances in Artificial Intelligence and Soft Computing

Print ISBN: 978-3-319-27059-3

Electronic ISBN: 978-3-319-27060-9

Copyright-Jahr: 2015

DOI
https://doi.org/10.1007/978-3-319-27060-9_21

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