Golden jackal optimization: A novel nature-inspired optimizer for engineering applications

作者：

Highlights：

• Developed Golden Jackal Optimization (GJO) Algorithm as an optimization method.

• Tested the performance of proposed algorithm against mathematical and engineering benchmarks.

• Compared proposed algorithm with other well-known optimization algorithms.

• Conducted statistical analyses.

• Demonstrated superiority of proposed algorithm in various conditions.

摘要

•Developed Golden Jackal Optimization (GJO) Algorithm as an optimization method.•Tested the performance of proposed algorithm against mathematical and engineering benchmarks.•Compared proposed algorithm with other well-known optimization algorithms.•Conducted statistical analyses.•Demonstrated superiority of proposed algorithm in various conditions.

论文关键词：Nature-inspired algorithm,Golden Jackal optimization,Metaheuristic,Constrained problems,Optimization algorithm,GJO

论文评审过程：Received 20 July 2020, Revised 5 March 2022, Accepted 15 March 2022, Available online 18 March 2022, Version of Record 23 March 2022.

论文官网地址：https://doi.org/10.1016/j.eswa.2022.116924