Modified Sand Cat Swarm Optimization Algorithm for Solving Constrained Engineering Optimization Problems

Di Wu, Honghua Rao, Changsheng Wen, Heming Jia (), Qingxin Liu and Laith Abualigah
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Di Wu: School of Education and Music, Sanming University, Sanming 365004, China
Honghua Rao: School of Information Engineering, Sanming University, Sanming 365004, China
Changsheng Wen: School of Information Engineering, Sanming University, Sanming 365004, China
Heming Jia: School of Information Engineering, Sanming University, Sanming 365004, China
Qingxin Liu: School of Computer Science and Technology, Hainan University, Haikou 570228, China
Laith Abualigah: Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman 19328, Jordan

Mathematics, 2022, vol. 10, issue 22, 1-41

Abstract: The sand cat swarm optimization algorithm (SCSO) is a recently proposed metaheuristic optimization algorithm. It stimulates the hunting behavior of the sand cat, which attacks or searches for prey according to the sound frequency; each sand cat aims to catch better prey. Therefore, the sand cat will search for a better location to catch better prey. In the SCSO algorithm, each sand cat will gradually approach its prey, which makes the algorithm a strong exploitation ability. However, in the later stage of the SCSO algorithm, each sand cat is prone to fall into the local optimum, making it unable to find a better position. In order to improve the mobility of the sand cat and the exploration ability of the algorithm. In this paper, a modified sand cat swarm optimization (MSCSO) algorithm is proposed. The MSCSO algorithm adds a wandering strategy. When attacking or searching for prey, the sand cat will walk to find a better position. The MSCSO algorithm with a wandering strategy enhances the mobility of the sand cat and makes the algorithm have stronger global exploration ability. After that, the lens opposition-based learning strategy is added to enhance the global property of the algorithm so that the algorithm can converge faster. To evaluate the optimization effect of the MSCSO algorithm, we used 23 standard benchmark functions and CEC2014 benchmark functions to evaluate the optimization performance of the MSCSO algorithm. In the experiment, we analyzed the data statistics, convergence curve, Wilcoxon rank sum test, and box graph. Experiments show that the MSCSO algorithm with a walking strategy and a lens position-based learning strategy had a stronger exploration ability. Finally, the MSCSO algorithm was used to test seven engineering problems, which also verified the engineering practicability of the proposed algorithm.

Keywords: sand cat swarm optimization algorithm; sound frequency; exploitation ability; wandering strategy; exploration ability; lens opposition-based learning strategy; engineering problem (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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