A Nonlinear Adaptive Weight-Based Mutated Whale Optimization Algorithm and Its Application for Solving Engineering Problems

Abstract

Whale optimization algorithm (WOA) is a swarm-based optimization algorithm with exceptional performance and significant originality. In this study, a novel variant of WOA called nonlinear adaptive weight-based mutated WOA (NAWMWOA) is proposed to overcome the shortcomings of original WOA such as easily falling into local optimum and slow convergence speed. In detail, the proposed NAWMWOA includes three novel strategies as comparing with original WOA. Firstly, a nonlinear convergence factor is embedded into the original WOA to balance exploration and exploitation ability. The second improvement is an adaptive weight strategy, which can enhance the exploratory searching trends and improve the solution accuracy. Moreover, the thirdly proposed hybrid mutation strategy has the function of increasing the accuracy and jumping out of the local optimum. The combination of the three strategies significantly improve convergence efficiency and search accuracy of original WOA. To verify the remarkable performance of the proposed NAWMWOA, a series of illustrious WOA variants and state-of-the-art intelligent algorithms is compared with the NAWMWOA on 37 benchmark functions and three typical engineering problems. The details of experimental and statistics results illustrate that the presented NAWMWOA has higher convergence efficiency and better solution accuracy. As a conclusion, the proposed NAWMWOA is a competitive and outstanding algorithm that can effectively solve optimization problems in practical engineering.

Keywords

• Whale optimization algorithm
• swarm intelligence
• adaptive weight
• engineering design