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

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.

[1]  Rui Mendes,et al.  Neighborhood topologies in fully informed and best-of-neighborhood particle swarms , 2006 .

[2]  Zong Woo Geem,et al.  A New Heuristic Optimization Algorithm: Harmony Search , 2001, Simul..

[3]  Yujun Zheng,et al.  A simplified water wave optimization algorithm , 2015, 2015 IEEE Congress on Evolutionary Computation (CEC).

[4]  Dan Simon,et al.  Biogeography-Based Optimization , 2022 .

[5]  Yanchun Liang,et al.  An improved genetic algorithm with variable population-size and a PSO-GA based hybrid evolutionary algorithm , 2003, Proceedings of the 2003 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.03EX693).

[6]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[7]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[8]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[9]  ChunXia Zhao,et al.  Particle swarm optimization with adaptive population size and its application , 2009, Appl. Soft Comput..

[10]  Dynamics of Surface Waves in Coastal Waters , 2009 .

[11]  Hu Huang.  Dynamics of Surface Waves in Coastal Waters: Wave-Current-Bottom Interactions , 2010 .

[12]  Janez Brest,et al.  Population size reduction for the differential evolution algorithm , 2008, Applied Intelligence.

[13]  Robert E. Smith,et al.  Adaptively Resizing Populations: An Algorithm and Analysis , 1993, ICGA.

[14]  Zbigniew Michalewicz,et al.  GAVaPS-a genetic algorithm with varying population size , 1994, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence.

[15]  Yujun Zheng.  Water wave optimization: A new nature-inspired metaheuristic , 2015, Comput. Oper. Res..

[16]  David H. Wolpert,et al.  No free lunch theorems for optimization , 1997, IEEE Trans. Evol. Comput..

[17]  V. K. Koumousis,et al.  A saw-tooth genetic algorithm combining the effects of variable population size and reinitialization to enhance performance , 2006, IEEE Transactions on Evolutionary Computation.

[18]  Patrick Siarry,et al.  A survey on optimization metaheuristics , 2013, Inf. Sci..

[19]  Ponnuthurai Nagaratnam Suganthan,et al.  Problem Definitions and Evaluation Criteria for the CEC 2014 Special Session and Competition on Single Objective Real-Parameter Numerical Optimization , 2014 .