Q-learning embedded sine cosine algorithm (QLESCA)

作者：

Highlights：

• QLESCA is a new variant of SCA that solves high-dimensional optimization problems.

• Q-Learning has been embedded into SCA to control the parameters, r1 and r3.

• QLESCA runs with a micro population, therefore, reduces the fitness evaluations.

• QLESCA outperforms four SCA variant algorithms and twelve swarm-based algorithms.

摘要

•QLESCA is a new variant of SCA that solves high-dimensional optimization problems.•Q-Learning has been embedded into SCA to control the parameters, r1 and r3.•QLESCA runs with a micro population, therefore, reduces the fitness evaluations.•QLESCA outperforms four SCA variant algorithms and twelve swarm-based algorithms.

论文关键词：Since cosine optimizer,Swarm intelligence,Q-learning algorithm,Optimization algorithms,Metaheuristic algorithm,Large-scale problems

论文评审过程：Received 25 February 2021, Revised 10 November 2021, Accepted 14 December 2021, Available online 26 December 2021, Version of Record 13 January 2022.

论文官网地址：https://doi.org/10.1016/j.eswa.2021.116417