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Ghodousian, Amin, Mollakazemiha, Mahdi, Karimian, Noushin. (1402). Perfectionism Search Algorithm (PSA): An Efficient Meta-Heuristic Optimization Approach. , 55(1), 11-36. doi: 10.22059/jac.2023.92492
Amin Ghodousian; Mahdi Mollakazemiha; Noushin Karimian. "Perfectionism Search Algorithm (PSA): An Efficient Meta-Heuristic Optimization Approach". , 55, 1, 1402, 11-36. doi: 10.22059/jac.2023.92492
Ghodousian, Amin, Mollakazemiha, Mahdi, Karimian, Noushin. (1402). 'Perfectionism Search Algorithm (PSA): An Efficient Meta-Heuristic Optimization Approach', , 55(1), pp. 11-36. doi: 10.22059/jac.2023.92492
Ghodousian, Amin, Mollakazemiha, Mahdi, Karimian, Noushin. Perfectionism Search Algorithm (PSA): An Efficient Meta-Heuristic Optimization Approach. , 1402; 55(1): 11-36. doi: 10.22059/jac.2023.92492

Perfectionism Search Algorithm (PSA): An Efficient Meta-Heuristic Optimization Approach

Journal of Algorithms and Computation
دوره 55، شماره 1، شهریور 2023، صفحه 11-36    اصل مقاله (849.69 K)
شناسه دیجیتال (DOI): 10.22059/jac.2023.92492
نویسندگان
Amin Ghodousian* 1؛ Mahdi Mollakazemiha2؛ Noushin Karimian3
1Faculty of Engineering Science, College of Engineering, University of Tehran, P.O.Box 11365-4563, Tehran, Iran.
2Department of Mathematical Sciences, Sharif University of Technology, Tehran, Iran.
3Department of Electrical and Electronic Engineering, The University of Manchester, Manchester, U
چکیده
This paper proposes a novel population-based meta-heuristic optimization algorithm, called Perfectionism Search
Algorithm (PSA), which is based on the psychological aspects of perfectionism. The PSA algorithm takes inspiration from one of the most popular model of perfectionism, which was proposed by Hewitt and Flett. During each iteration of the PSA algorithm, new solutions are generated by mimicking different types and aspects of perfectionistic behavior. In order to have a complete perspective on the performance of PSA, the proposed algorithm is tested with various nonlinear optimization problems, through selection of 35 benchmark functions from the literature. The generated solutions for these problems, were also compared with 11 well-known meta-heuristics which had been applied to many complex and
practical engineering optimization problems. The obtained results confirm the high performance of the proposed
algorithm in comparison to the other well-known algorithms.
 
کلیدواژه‌ها
Nonlinear optimization؛ global optimization؛ meta-heuristics؛ perfectionism؛ population-based algorithms؛ evolutionary algorithms؛ benchmark test functions
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