A majority–minority cellular automata algorithm for global optimization

作者：

Highlights：

• Majority and minority CA are useful to aim an algorithm for global optimization.

• The new algorithm (MmCAA) is very simple and easy to implement.

• MmCAA is inspired in the neighborhood concept and concurrent rules of CA.

• MmCAA is competitive to optimize test problems and engineering applications.

摘要

•Majority and minority CA are useful to aim an algorithm for global optimization.•The new algorithm (MmCAA) is very simple and easy to implement.•MmCAA is inspired in the neighborhood concept and concurrent rules of CA.•MmCAA is competitive to optimize test problems and engineering applications.

论文关键词：Global optimization,Majority cellular automata,Metaheuristics,Engineering applications

论文评审过程：Received 26 August 2021, Revised 28 January 2022, Accepted 25 April 2022, Available online 6 May 2022, Version of Record 13 May 2022.

论文官网地址：https://doi.org/10.1016/j.eswa.2022.117379