A novel whale optimisation algorithm with filtering disturbance and nonlinear step
by Jinkun Luo; Fazhi He; Haoran Li; Xian-Tao Zeng; Yaqian Liang
International Journal of Bio-Inspired Computation (IJBIC), Vol. 20, No. 2, 2022

Abstract: As a recent addition of population-based metaheuristic algorithms, whale optimisation algorithm (WOA) has attracted a lot of attention recently. However, WOA still has room for improvement in the accuracy and reliability of solution. In this study, a novel WOA with filtering disturbance and nonlinear step (FDNS-WOA) is proposed. Firstly, to enhance the population diversity and the global search ability, we design a weighted Cauchy mutation equation to perturb solution space. Secondly, the excessive disturbance may cause the imbalance between exploration and exploitation, and the quality of solutions will be decreased. Thus, before the disturbance, we propose a filtering mechanism to dynamically select different individuals for disturbance at different evolutionary stages. Thirdly, a weighted local walk mechanism is designed to improve the local exploitation capability. Different from original linear step with large fluctuation, we propose a nonlinear step to reduce blind spots in the local exploitation process. Finally, the proposed FDNS-WOA is tested on benchmark functions and applied in a real world problem. The experimental results show that the proposed FDNS-WOA not only outperforms other recent metaheuristic algorithms in term of accuracy and reliability for most benchmark functions, but also achieves satisfactory results in real world problem.

Online publication date: Mon, 07-Nov-2022

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