PSCSO: enhanced sand cat swarm optimization inspired by the political system to solve complex problems
Yükleniyor...
Tarih
2023
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Elsevier
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
The Sand Cat Swarm Optimization (SCSO) algorithm is a recently introduced metaheuristic with balanced behavior in the exploration and exploitation phases. However, it is not fast in convergence and may not be successful in finding the global optima, especially for complex problems since it starts the exploitation phase late. Moreover, the performance of SCSO is also affected by incorrect position as it depends on the location of the global optimum. Therefore, this study proposes a new method for the SCSO algorithm with a multidisciplinary principle inspired by the Political (Parliamentary) system, which is named PSCSO. The suggested algorithm increases the chances of finding the global solution by randomly choosing positions between the position of the candidate's best solution available so far and the current position during the exploitation phase. In this regard, a new coefficient is defined that affects the exploration and exploitation phases. In addition, a new mathematical model is introduced to use in the exploitation phase. The performance of the PSCSO algorithm is analyzed on a total of 41 benchmark functions from CEC2015, 2017, and 2019. In addition, its performance is evaluated in four classical engineering problems. The proposed algorithm is compared with the SCSO, Stochastic variation and Elite collaboration in SCSO (SE-SCSO), Hybrid SCSO (HSCSO), Parliamentary Optimization Algorithm (POA), and Arithmetic Optimization Algorithm (AOA) algorithms, which have been proposed in recent years. The ob-tained results depict that the PSCSO algorithm performs better or equivalently to the compared optimization algorithms.
Açıklama
Anahtar Kelimeler
Optimization, Metaheuristics, Parliamentary System, Sand Cat Swarm Optimization
Kaynak
Advances in Engineering Software
WoS Q Değeri
Q1
Scopus Q Değeri
Cilt
178
Sayı
Künye
Kiani, F., Anka, F. A., & Erenel, F. (2023). PSCSO: Enhanced sand cat swarm optimization inspired by the political system to solve complex problems. Advances in Engineering Software, 178, 103423.
