Chaotic Sand Cat Swarm Optimization

Farzad Kiani (), Sajjad Nematzadeh, Fateme Aysin Anka and Mine Afacan Findikli
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Farzad Kiani: Computer Engineering Department, Faculty of Engineering, Fatih Sultan Mehmet Vakif University, 34445 Istanbul, Turkey
Sajjad Nematzadeh: Computer Engineering Department, Faculty of Engineering, Istanbul Topkapi University, 34087 Istanbul, Turkey
Fateme Aysin Anka: Political Sciences and Public Administration Department, Faculty of Economics, Administrative and Social Sciences, Istinye University, 34396 Istanbul, Turkey
Mine Afacan Findikli: Business Administration Department, Faculty of Economics, Administrative and Social Sciences, Istinye University, 34396 Istanbul, Turkey

Mathematics, 2023, vol. 11, issue 10, 1-47

Abstract: In this study, a new hybrid metaheuristic algorithm named Chaotic Sand Cat Swarm Optimization (CSCSO) is proposed for constrained and complex optimization problems. This algorithm combines the features of the recently introduced SCSO with the concept of chaos. The basic aim of the proposed algorithm is to integrate the chaos feature of non-recurring locations into SCSO’s core search process to improve global search performance and convergence behavior. Thus, randomness in SCSO can be replaced by a chaotic map due to similar randomness features with better statistical and dynamic properties. In addition to these advantages, low search consistency, local optimum trap, inefficiency search, and low population diversity issues are also provided. In the proposed CSCSO, several chaotic maps are implemented for more efficient behavior in the exploration and exploitation phases. Experiments are conducted on a wide variety of well-known test functions to increase the reliability of the results, as well as real-world problems. In this study, the proposed algorithm was applied to a total of 39 functions and multidisciplinary problems. It found 76.3% better responses compared to a best-developed SCSO variant and other chaotic-based metaheuristics tested. This extensive experiment indicates that the CSCSO algorithm excels in providing acceptable results.

Keywords: Chaotic Sand Cat Swarm Optimization; chaotic maps; constrained problems; multidisciplinary problems; hybrid metaheuristics (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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