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 code is also available at https://github.com/qamar-askari/HBO.
This is the source code of "Askari Q, Saeed M, Younas I. Heap-based optimizer inspired by corporate rank hierarchy for global optimization. Expert Systems with Applications. 2020 Jul 18" https://doi.org/10.1016/j.eswa.2020.113702.
My Homepage: http://qamaraskari.com/
The link to download the paper without subscription is available at the homepage. The Latex sources and MATLAB implementation of my algorithms and benchmark functions are also available at my homepage. I'm open to collaborate if you are interested in to work on my algorithms and enhance them or hybridize them with existing techniques or apply them to solve real-world applications. My research interests and current projects are also available at my homepage.
Qamar Askari (2020). Heap-Based Optimizer (HBO) (https://www.mathworks.com/matlabcentral/fileexchange/78492-heap-based-optimizer-hbo), MATLAB Central File Exchange. Retrieved .
Askari, Qamar, et al. “Heap-Based Optimizer Inspired by Corporate Rank Hierarchy for Global Optimization.” Expert Systems with Applications, vol. 161, Elsevier BV, Dec. 2020, p. 113702, doi:10.1016/j.eswa.2020.113702.
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