Abstract
Water wave optimization (WWO) is a nature-inspired metaheuristic that simulates propagation, refraction and breaking of shallow water waves to search the global optimal solution in the search space. To suppress premature convergence and further accelerate the speed of the basic WWO, we propose an improved WWO with a new distributed-learning refraction operator (called DLWWO), which makes stationary waves learn from more better waves to increase the solution diversity. DLWWO also adopts a nonlinear dimension reduction strategy in the propagation operator to accelerate the search process. We test the DLWWO algorithm on 15 function optimization problems from the CEC2015 single-objective optimization test suite. Experimental results show that DLWWO exhibits very competitive performance compared to the original WWO and other comparative algorithms, which validates the effectiveness and efficiency of the two new proposed strategies.
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Liao, MH., Chen, X., Zheng, YJ. (2022). Water Wave Optimization with Distributed-Learning Refraction. In: Pan, L., Cui, Z., Cai, J., Li, L. (eds) Bio-Inspired Computing: Theories and Applications. BIC-TA 2021. Communications in Computer and Information Science, vol 1565. Springer, Singapore. https://doi.org/10.1007/978-981-19-1256-6_14
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DOI: https://doi.org/10.1007/978-981-19-1256-6_14
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