Enhanced sine cosine algorithm with crossover: A comparative study and empirical analysis

作者：

Highlights：

• The enhanced sine cosine algorithm (ECr-SCA) is proposed for global optimization.

• A greedy search and modified transition parameter are used to increase exploitation.

• The multi-parent crossover is embedded to enhance diversity and global search.

• The excellent performance of the ECr-SCA is validated on benchmark problems.

• The ECr-SCA is also used to train multilayer perceptron.

摘要

•The enhanced sine cosine algorithm (ECr-SCA) is proposed for global optimization.•A greedy search and modified transition parameter are used to increase exploitation.•The multi-parent crossover is embedded to enhance diversity and global search.•The excellent performance of the ECr-SCA is validated on benchmark problems.•The ECr-SCA is also used to train multilayer perceptron.

论文关键词：Metaheuristics,Sine cosine algorithm,Crossover,Multi-layer perceptron

论文评审过程：Received 23 July 2020, Revised 3 March 2022, Accepted 7 March 2022, Available online 19 March 2022, Version of Record 22 March 2022.

论文官网地址：https://doi.org/10.1016/j.eswa.2022.116856