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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Acknowledgements
The work is supported by grants from National Natural Science Foundation (No. 61473263) and Zhejiang Provincial Natural Science Foundation (No. LY14F030011) of China.
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Zhang, B., Zhang, MX., Zhang, JF., Zheng, YJ. (2015). A Water Wave Optimization Algorithm with Variable Population Size and Comprehensive Learning. In: Huang, DS., Bevilacqua, V., Premaratne, P. (eds) Intelligent Computing Theories and Methodologies. ICIC 2015. Lecture Notes in Computer Science(), vol 9225. Springer, Cham. https://doi.org/10.1007/978-3-319-22180-9_13
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DOI: https://doi.org/10.1007/978-3-319-22180-9_13
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