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

Abstract

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 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 the WDNMWOA has better convergence accuracy and strong optimization ability than the original WOA.

An accelerated sine mapping whale optimizer for feature selection

“…Qiao et al. proposed to introduce individual disturbance and neighborhood mutation (WDNMWOA) 61 to avoid WOA from falling into local optima. The BWOA 82 with Lévy flight and chaotic local search is prominent in constrained engineering design problems.…”

“…These heuristics and improved algorithms have demonstrated significant potential in many application scenarios, such as engineering design problems, 61 , 62 , 63 image segmentation, 64 , 65 , 66 , 67 , 68 scheduling problems, 69 feature selection, 70 , 71 , 72 and financial stress prediction. 21 , 73 Many practices indicate that the enhanced approach performs better than the original algorithm in some optimization domains.…”

Intradialytic hypotension prediction using covariance matrix-driven whale optimizer with orthogonal structure-assisted extreme learning machine

“…Furthermore, an intelligent HD management system will be built based on an improved algorithm. In addition, we will also explore other application areas of COWOA, such as image segmentation (Liu L. et al, 2021;Zhao D. et al, 2021), engineering optimization (Qi et al, 2022;Qiao et al, 2022), resource allocation (Deng et al, 2022a(Deng et al, ). 10.3389/fninf.2022 Health Committee (2021PY054), and the Basic Scientific Research Projects of Wenzhou Science and Technology Bureau (Y2020026).…”

“…Furthermore, an intelligent HD management system will be built based on an improved algorithm. In addition, we will also explore other application areas of COWOA, such as image segmentation ( Liu L. et al, 2021 ; Zhao D. et al, 2021 ), engineering optimization ( Qi et al, 2022 ; Qiao et al, 2022 ), resource allocation ( Deng et al, 2022a ).…”

Chaotic opposition learning with mirror reflection and worst individual disturbance grey wolf optimizer for continuous global numerical optimization

“…During the process of surrounding their prey, Grey wolves encounter both the chance of successfully encircling the prey and the potential risk of the prey evading capture. This phenomenon is accurately modelled in the HHO algorithm that mimics the hunting behaviour of Harris hawks when they catch rabbits 59 . In HHO, there is a probability that the rabbit being chased by the hawk may escape.…”