Hybrid advanced player selection strategy based population search for global optimization
作者：
Highlights：
• Interactive, bi-level, sports inspired cognitive learning introduced.
• Chaotic-map, pool topology framework, two stage multiswarm learning for exploration.
• Blended laplacian operator based hybrid swarm-evolutionary process for exploitation.
• Team reformation, recruit-eliminate outperforms other state-of-the-art algorithms.
• Proposed algorithm solves real parameter optimization and engineering problem well.
摘要
•Interactive, bi-level, sports inspired cognitive learning introduced.•Chaotic-map, pool topology framework, two stage multiswarm learning for exploration.•Blended laplacian operator based hybrid swarm-evolutionary process for exploitation.•Team reformation, recruit-eliminate outperforms other state-of-the-art algorithms.•Proposed algorithm solves real parameter optimization and engineering problem well.
论文关键词：Particle swarm optimization,Differential evolution,Artificial bee colony,Hybrid framework,Human cognizance,Global optimization
论文评审过程：Received 27 November 2018, Revised 7 July 2019, Accepted 18 July 2019, Available online 1 August 2019, Version of Record 8 August 2019.
论文官网地址：https://doi.org/10.1016/j.eswa.2019.112825
