Novel enhanced Salp Swarm Algorithms using opposition-based learning schemes for global optimization problems

作者：

Highlights：

• Enhanced Salp Swarm Algorithms using Opposition-Based Learning schemes.

• The proposed algorithms are tested on 15 IEEE CEC2013 benchmark problems.

• In-depth investigation regarding exploitation, exploration, and convergence.

• The proposed algorithms perform better than classical Salp Swarm Algorithm.

摘要

•Enhanced Salp Swarm Algorithms using Opposition-Based Learning schemes.•The proposed algorithms are tested on 15 IEEE CEC2013 benchmark problems.•In-depth investigation regarding exploitation, exploration, and convergence.•The proposed algorithms perform better than classical Salp Swarm Algorithm.

论文关键词：Salp Swarm Algorithm,Swarm intelligence,Metaheuristic,Opposition-based Learning,Optimization

论文评审过程：Received 8 March 2021, Revised 6 June 2022, Accepted 21 June 2022, Available online 27 June 2022, Version of Record 7 July 2022.

论文官网地址：https://doi.org/10.1016/j.eswa.2022.117961