Heap-based optimizer inspired by corporate rank hierarchy for global optimization
作者：
Highlights：
• Heap-based optimizer (HBO) inspired by corporate rank hierarchy (CRH) is proposed.
• HBO utilizes heap to map the hierarchy and model equations for 3 CRH activities.
• A parameter (γ) to escape local optima without lacking exploitation is introduced.
• Exploration and exploitation are balanced through self-adaptive parameters.
• Performance is evaluated on 97 benchmarks and 3 mechanical engineering problems.
摘要
•Heap-based optimizer (HBO) inspired by corporate rank hierarchy (CRH) is proposed.•HBO utilizes heap to map the hierarchy and model equations for 3 CRH activities.•A parameter (γ) to escape local optima without lacking exploitation is introduced.•Exploration and exploitation are balanced through self-adaptive parameters.•Performance is evaluated on 97 benchmarks and 3 mechanical engineering problems.
论文关键词：Social optimization algorithm,Corporate hierarchy based optimization,Nature-inspired meta-heuristic,Global optimization algorithm
论文评审过程：Received 22 September 2019, Revised 5 June 2020, Accepted 26 June 2020, Available online 18 July 2020, Version of Record 25 July 2020.
论文官网地址：https://doi.org/10.1016/j.eswa.2020.113702
