A novel metaheuristic optimizer inspired by behavior of jellyfish in ocean

作者：

Highlights：

• An artificial Jellyfish Search (JS) optimizer inspired by jellyfish behavior is proposed.

• JS has only two control parameters, which are population size and number of iterations.

• The new algorithm is successfully tested on benchmark functions and optimization problems.

• JS optimizer outperforms well-known metaheuristic algorithms and prior studies.

摘要

•An artificial Jellyfish Search (JS) optimizer inspired by jellyfish behavior is proposed.•JS has only two control parameters, which are population size and number of iterations.•The new algorithm is successfully tested on benchmark functions and optimization problems.•JS optimizer outperforms well-known metaheuristic algorithms and prior studies.

论文关键词：Design of metaheuristic algorithm,Bio-inspired swarm intelligence,Jellyfish search optimizer,Numerical computation,Benchmark functions,Engineering design

论文评审过程：Received 5 February 2020, Revised 22 May 2020, Accepted 12 July 2020, Available online 7 August 2020, Version of Record 7 August 2020.

论文官网地址：https://doi.org/10.1016/j.amc.2020.125535