Heap-based optimizer based on three new updating strategies
作者：
Highlights：
• Three new search strategies are proposed to update the 3 agents’ positions.
• A differential self-learning search strategy is proposed for the best agent.
• A best example learning search strategy is adopted for a sub-best agent.
• An opposition differential perturbation strategy is used for an ordinary agent.
• Proposed algorithm is more effective on 90 functions and some real-life problems.
摘要
•Three new search strategies are proposed to update the 3 agents’ positions.•A differential self-learning search strategy is proposed for the best agent.•A best example learning search strategy is adopted for a sub-best agent.•An opposition differential perturbation strategy is used for an ordinary agent.•Proposed algorithm is more effective on 90 functions and some real-life problems.
论文关键词：Meta-heuristic algorithm,Heap-based optimizer,Updating strategies,K-means clustering,Engineering problems
论文评审过程：Received 14 July 2021, Revised 15 June 2022, Accepted 17 July 2022, Available online 30 July 2022, Version of Record 8 August 2022.
论文官网地址：https://doi.org/10.1016/j.eswa.2022.118222
