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Design of an Adaptive Support Vector Regressor Controller for a Spherical Tank System Read chapter Read first chapter

2015 | OriginalPaper | Chapter

Multi-Island Competitive Cooperative Coevolution for Real Parameter Global Optimization

Authors : Kavitesh K. Bali, Rohitash Chandra

Published in: Neural Information Processing

Publisher: Springer International Publishing

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Abstract

Problem decomposition is an important attribute of cooperative coevolution that depends on the nature of the problems in terms of separability which is defined by the level of interaction amongst decision variables. Recent work in cooperative coevolution featured competition and collaboration of problem decomposition methods that was implemented as islands in a method known as competitive island cooperative coevolution (CICC). In this paper, a multi-island competitive cooperative coevolution algorithm (MICCC) is proposed in which several different problem decomposition strategies are given a chance to compete, collaborate and motivate other islands while converging to a common solution. The performance of MICCC is evaluated on eight different benchmark functions and are compared with CICC where only two islands were utilized. The results from the experimental analysis show that competition and collaboration of several different island can yield solutions with a quality better than the two-island competition algorithm (CICC) on most complex multi-modal problems.

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previous chapter Lagrange Programming Neural Network for the -norm Constrained Quadratic Minimization
next chapter Competitive Island-Based Cooperative Coevolution for Efficient Optimization of Large-Scale Fully-Separable Continuous Functions
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Metadata
Title
Multi-Island Competitive Cooperative Coevolution for Real Parameter Global Optimization
Authors
Kavitesh K. Bali
Rohitash Chandra
Copyright Year
2015
Book
Neural Information Processing

Print ISBN: 978-3-319-26554-4

Electronic ISBN: 978-3-319-26555-1

Copyright Year: 2015

DOI
https://doi.org/10.1007/978-3-319-26555-1_15

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