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2015 | OriginalPaper | Chapter

Scaling up Multi-island Competitive Cooperative Coevolution for Real Parameter Global Optimisation

Authors : Kavitesh K. Bali, Rohitash Chandra

Published in: AI 2015: Advances in Artificial Intelligence

Publisher: Springer International Publishing

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Abstract

A major challenge in using cooperative coevolution (CC) for global optimisation is the decomposition of a given problem into subcomponents. Variable interaction is a major constraint that determines the decomposition strategy of a problem. Hence, finding an optimal decomposition strategy becomes a burdensome task as inter-dependencies between decision variables are unknown for these problems. In recent related work, a multi-island competitive cooperative coevolution (MICCC) algorithm was introduced which featured competition and collaboration of several different decomposition strategies. MICCC used five different uniform problem decomposition strategies that were implemented as independent islands. This paper presents an analysis of the MICCC algorithm and also extends it to more than five islands. We incorporate arbitrary (non-uniform) problem decomposition strategies as additional islands in MICCC and monitor how each different problem decomposition strategy contributes towards the global fitness over different stages of optimisation.

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Metadata
Title
Scaling up Multi-island Competitive Cooperative Coevolution for Real Parameter Global Optimisation
Authors
Kavitesh K. Bali
Rohitash Chandra
Copyright Year
2015
Book
AI 2015: Advances in Artificial Intelligence

Print ISBN: 978-3-319-26349-6

Electronic ISBN: 978-3-319-26350-2

Copyright Year: 2015

DOI
https://doi.org/10.1007/978-3-319-26350-2_4

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