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Erschienen in:

2017 | OriginalPaper | Buchkapitel

A Hybrid Particle Swarm Optimization for High-Dimensional Dynamic Optimization

verfasst von : Wenjian Luo, Bin Yang, Chenyang Bu, Xin Lin

Erschienen in: Simulated Evolution and Learning

Verlag: Springer International Publishing

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Abstract

High-Dimensional Dynamic Optimization Problems (HDDOPs) commonly exist in real-world applications. In evolutionary computation field, most of existing benchmark problems, which could simulate HDDOPs, are non-separable. Thus, we give a novel benchmark problem, called high-dimensional moving peaks benchmark to simulate separable, partially separable, and non-separable problems. Moreover, a hybrid Particle Swarm Optimization algorithm based on Grouping, Clustering and Memory strategies, i.e. GCM-PSO, is proposed to solve HDDOPs. In GCM-PSO, a differential grouping method is used to decompose a HDDOP into a number of sub-problems based on variable interactions firstly. Then each sub-problem is solved by a species-based particle swarm optimization, where the nearest better clustering is adopted as the clustering method. In addition, a memory strategy is also adopted in GCM-PSO. Experimental results show that GCM-PSO performs better than the compared algorithms in most cases.

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Vorheriges Kapitel A General Swarm Intelligence Model for Continuous Function Optimization

Nächstes Kapitel Visualizing the Search Dynamics in a High-Dimensional Space for a Particle Swarm Optimizer

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Titel: A Hybrid Particle Swarm Optimization for High-Dimensional Dynamic Optimization
verfasst von: Wenjian Luo
Bin Yang
Chenyang Bu
Xin Lin
Verlag: Springer International Publishing
Buch: Simulated Evolution and Learning
Print ISBN: 978-3-319-68758-2

Electronic ISBN: 978-3-319-68759-9

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-3-319-68759-9_81

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