Individual disturbance and neighborhood mutation search enhanced whale optimization: performance design for engineering problems

The whale optimizer is a popular metaheuristic algorithm, which has the problems of weak global exploration, easy falling into local optimum, and low optimization accuracy when searching for the optimal solution. To solve these problems, this paper proposes an enhanced whale optimization algorithm (WOA) based on the worst individual disturbance (WD) and neighborhood mutation search (NM), named WDNMWOA, which employed WD to enhance the ability to jump out of local optimum and global exploration, adopted NM to enhance the possibility of individuals approaching the optimal solution. The superiority of WDNMWOA is demonstrated by representative IEEE CEC2014, CEC2017, CEC2019, and CEC2020 benchmark functions and four engineering examples. The experimental results show that thes WDNMWOA has better convergence accuracy and strong optimization ability than the original WOA.

Automated summary

This paper proposes an enhanced whale optimization algorithm (WDNMWOA) based on worst individual disturbance (WD) and neighborhood mutation search (NM) to address the issues of weak global exploration and low optimization accuracy in the original algorithm. The experimental results demonstrate that WDNMWOA has better convergence accuracy and stronger optimization ability compared to the original WOA.