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2016 | OriginalPaper | Chapter

A Hybrid Group Search Optimizer with Opposition-Based Learning and Differential Evolution

Authors : Chengwang Xie, Wenjing Chen, Weiwei Yu

Published in: Computational Intelligence and Intelligent Systems

Publisher: Springer Singapore

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Abstract

Group search optimizer (GSO) is a recently developed heuristic inspired by biological group search resources behavior. However, it still has some defects such as slow convergence speed and poor accuracy of solution. In order to improve the performance of GSO in solving complex optimization problems, an opposition-based learning approach (OBL) and a differential evolution method (DE) are integrated into GSO to form a hybrid GSO. In this paper, the strategy of OBL is used to enlarge the search region, and the operator of DE is utilized to enhance local search to improve. Comparison experiments have demonstrated that our hybrid GSO algorithm performed advantages over previous GSO and DE approaches in convergence speed and accuracy of solution.

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next chapter A New Firefly Algorithm with Local Search for Numerical Optimization
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Metadata
Title
A Hybrid Group Search Optimizer with Opposition-Based Learning and Differential Evolution
Authors
Chengwang Xie
Wenjing Chen
Weiwei Yu
Copyright Year
2016
Publisher
Springer Singapore
Book
Computational Intelligence and Intelligent Systems

Print ISBN: 978-981-10-0355-4

Electronic ISBN: 978-981-10-0356-1

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-981-10-0356-1_1

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