Hybridizing ant lion with whale optimization algorithm for compressed sensing MR image reconstruction via l1 minimization: an ALWOA strategy

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.

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Hybridizing ant lion with whale optimization algorithm for compressed sensing MR image reconstruction via l1 minimization: an ALWOA strategy

Cited by 5 publications

References 27 publications

Recent advances of whale optimization algorithm, its versions and applications

Recent advances of whale optimization algorithm, its versions and applications

Optimal blind colour image watermarking based on adaptive chaotic grasshopper optimization algorithm

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

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