Social mimic optimization algorithm and engineering applications

作者：

Highlights：

• A novel SMO inspired by mimicking behavior to solve optimization problems is presented.

• SMO includes a mimic operator to simulate search in the response space.

• SMO does not require control operator respect to other Meta heuristic methods.

• SMO solve optimization problems with minimum population size.

• SMO is compared with 14 well-known and state of the art optimization algorithms.

摘要

•A novel SMO inspired by mimicking behavior to solve optimization problems is presented.•SMO includes a mimic operator to simulate search in the response space.•SMO does not require control operator respect to other Meta heuristic methods.•SMO solve optimization problems with minimum population size.•SMO is compared with 14 well-known and state of the art optimization algorithms.

论文关键词：Meta-heuristic,Optimization,Swarm intelligence,Social mimic optimization

论文评审过程：Received 12 August 2018, Revised 10 May 2019, Accepted 25 May 2019, Available online 27 May 2019, Version of Record 6 June 2019.

论文官网地址：https://doi.org/10.1016/j.eswa.2019.05.035