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2018 | OriginalPaper | Chapter

A Conical Area Differential Evolution with Dual Populations for Constrained Optimization

Authors : Bin Wu, Weiqin Ying, Yu Wu, Yuehong Xie, Zhenyu Wang

Published in: Computational Intelligence and Intelligent Systems

Publisher: Springer Singapore

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Abstract

During the last decade, multi-objective approaches to dealing with constraints in evolutionary algorithms have drawn more and more attention from researchers. In this paper, a conical area differential evolution algorithm (CADE) with dual populations is proposed for constrained optimization by borrowing the ideas of cone decomposition for bi-objective optimization. In CADE, a conical sub-population and a feasible one are designed to search the global feasible optimum along the Pareto front and the feasible segment, respectively. The conical sub-population aims to construct and utilize the Pareto front by a biased cone decomposition strategy in geometric proportion and a conical area indicator. Afterwards, neighbors in both sub-populations are adequately exploited to help each other. 13 benchmark test instances are used to assess the performance of CADE. The result reveals that CADE is capable of producing significantly competitive solutions for constraint optimization problems compared with the other popular approaches.

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Metadata
Title
A Conical Area Differential Evolution with Dual Populations for Constrained Optimization
Authors
Bin Wu
Weiqin Ying
Yu Wu
Yuehong Xie
Zhenyu Wang
Copyright Year
2018
Publisher
Springer Singapore
Book
Computational Intelligence and Intelligent Systems

Print ISBN: 978-981-13-1650-0

Electronic ISBN: 978-981-13-1651-7

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-981-13-1651-7_5

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