PSCSO: Enhanced sand cat swarm optimization inspired by the political system to solve complex problems. (April 2023)

• Record Type: Journal Article
• Authors: Kiani, Farzad; Anka, Fateme Aysin; Erenel, Fahri
• Is Part Of: Advances in engineering software. Volume 178(2023)
• Publication Date: 2023-04
• Subjects: Optimization -- Metaheuristics -- Parliamentary System -- Sand Cat Swarm Optimization; Computer-aided engineering -- Periodicals; Engineering -- Computer programs -- Periodicals; Engineering -- Software -- Periodicals; Periodicals; 620.0028553
• DOI: 10.1016/j.advengsoft.2023.103423
• Languages: English
• ISSNs: 0965-9978

Abstract

Highlights: A new variant for SCSO for solving the problem of slow convergence and late exploitation. Updating sand cat agent positions inspired by the political system. Providing broader searches in the search area based on the cross-party membership switching mechanism. Better than other well-known algorithms in four different problem types. Has suitable behavior in exploration and exploitation phases. Abstract: 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 obtained results depict that the PSCSO algorithm performs better or equivalently to the compared optimization algorithms.