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Skill Optimization Algorithm: A New Human-Based Metaheuristic Technique

Hadi Givi1, Marie Hubalovska2,*
1 Department of Electrical Engineering, Shahreza Campus, University of Isfahan, Iran
2 Department of Technics, Faculty of Education, University of Hradec Kralove, Czech Republic
* Corresponding Author: Marie Hubalovska. Email:

Computers, Materials & Continua 2023, 74(1), 179-202. https://doi.org/10.32604/cmc.2023.030379

Received 24 March 2022; Accepted 16 May 2022; Issue published 22 September 2022

Abstract

Metaheuristic algorithms are widely used in solving optimization problems. In this paper, a new metaheuristic algorithm called Skill Optimization Algorithm (SOA) is proposed to solve optimization problems. The fundamental inspiration in designing SOA is human efforts to acquire and improve skills. Various stages of SOA are mathematically modeled in two phases, including: (i) exploration, skill acquisition from experts and (ii) exploitation, skill improvement based on practice and individual effort. The efficiency of SOA in optimization applications is analyzed through testing this algorithm on a set of twenty-three standard benchmark functions of a variety of unimodal, high-dimensional multimodal, and fixed-dimensional multimodal types. The optimization results show that SOA, by balancing exploration and exploitation, is able to provide good performance and appropriate solutions for optimization problems. In addition, the performance of SOA in optimization is compared with ten metaheuristic algorithms to evaluate the quality of the results obtained by the proposed approach. Analysis and comparison of the obtained simulation results show that the proposed SOA has a superior performance over the considered algorithms and achieves much more competitive results.

Keywords

Optimization; human-based; skill; exploration; exploitation; metaheuristic algorithm

Cite This Article

H. Givi and M. Hubalovska, "Skill optimization algorithm: a new human-based metaheuristic technique," Computers, Materials & Continua, vol. 74, no.1, pp. 179–202, 2023.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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