Abstract
Water wave optimization (WWO) is a recently proposed nature-inspired algorithm that mimics shallow water wave motions to solve optimization problems. In this paper, we propose an improved WWO algorithm with a new self-adaptive directed propagation operator, which dynamically adjusts the propagation direction of each solution according to the fitness change caused by the last propagation operation to improve local search ability. The new algorithm also adopts a nonlinear population reduction strategy to better balance the local search and global search abilities. Experimental results on 15 function optimization problems from the CEC2015 single-objective optimization test suite show that the improved algorithm exhibits significantly better performance than the original WWO and some other evolutionary algorithms including particle swarm optimization (PSO) and biogeography-based optimization (BBO), which validates the effectiveness and efficiency of the proposed strategies.
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