Heap-based optimizer inspired by corporate rank hierarchy for global optimization

• Published in: Expert systems with applications Vol. 161; p. 113702
• Main Authors: Askari, Qamar, Saeed, Mehreen, Younas, Irfan
• Format: Journal Article
• Language: English
• Published: New York Elsevier Ltd 15.12.2020; Elsevier BV
• Subjects: Algorithms; Benchmarks; Corporate hierarchy based optimization; Corporate structure; Data structures; Global optimization; Global optimization algorithm; Mechanical engineering; Nature-inspired meta-heuristic; Optimization; Rank tests; Social optimization algorithm; Source code; Nature-inspired meta-heuristic; Social optimization algorithm; Corporate hierarchy based optimization; Global optimization algorithm
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2020.113702

•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. In an organization, a group of people working for a common goal may not achieve their goal unless they organize themselves in a hierarchy called Corporate Rank Hierarchy (CRH). This principle motivates us to map the concept of CRH to propose a new algorithm for optimization that logically arranges the search agents in a hierarchy based on their fitness. The proposed algorithm is named as heap-based optimizer (HBO) because it utilizes the heap data structure to map the concept of CRH. The mathematical model of HBO is built on three pillars: the interaction between the subordinates and their immediate boss, the interaction between the colleagues, and self-contribution of the employees. The proposed algorithm is benchmarked with 97 diverse test functions including 29 CEC-BC-2017 functions with very challenging landscapes against 7 highly-cited optimization algorithms including the winner of CEC-BC-2017 (EBO-CMAR). In the first two experiments, the exploitative and explorative behavior of HBO is evaluated by using 24 unimodal and 44 multimodal functions, respectively. It is shown through experiments and Friedman mean rank test that HBO outperforms and secures 1st rank. In the third experiment, we use 29 CEC-BC-2017 benchmark functions. According to Friedman mean rank test HBO attains 2nd position after EBO-CMAR; however, the difference in ranks of HBO and EBO-CMAR is shown to be statistically insignificant by using Bonferroni method based multiple comparison test. Moreover, it is shown through the Friedman test that the overall rank of HBO is 1st for all 97 benchmarks. In the fourth and the last experiment, the applicability on real-world problems is demonstrated by solving 3 constrained mechanical engineering optimization problems. The performance is shown to be superior or equivalent to the other algorithms, which have been used in the literature. The source code of HBO is publicly available athttps://github.com/qamar-askari/HBO.