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2015 | OriginalPaper | Chapter

A Water Wave Optimization Algorithm with Variable Population Size and Comprehensive Learning

Authors : Bei Zhang, Min-Xia Zhang, Jie-Feng Zhang, Yu-Jun Zheng

Published in: Intelligent Computing Theories and Methodologies

Publisher: Springer International Publishing

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Abstract

Water wave optimization (WWO) is a new nature-inspired metaheuristic by mimicking shallow water wave motions including propagation, refraction, and breaking. In this paper we present a variation of WWO, named VC-WWO, which adopts a variable population size to accelerate the search process, and develops a comprehensive learning mechanism in the refraction operator to make stationary waves learn from more exemplars to increase the solution diversity, and thus provides a much better tradeoff between exploration and exploitation. Experimental results show that the overall performance of VC-WWO is better than the original WWO and other comparative algorithms on the CEC 2015 single-objective optimization test problems, which validates the effectiveness of the two new strategies proposed in the paper.

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Metadata
Title
A Water Wave Optimization Algorithm with Variable Population Size and Comprehensive Learning
Authors
Bei Zhang
Min-Xia Zhang
Jie-Feng Zhang
Yu-Jun Zheng
Copyright Year
2015
Book
Intelligent Computing Theories and Methodologies

Print ISBN: 978-3-319-22179-3

Electronic ISBN: 978-3-319-22180-9

Copyright Year: 2015

DOI
https://doi.org/10.1007/978-3-319-22180-9_13

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