Large-Scale Competitive Learning-Based Salp Swarm for Global Optimization and Solving Constrained Mechanical and Engineering Design Problems

Mohammed Qaraad, Abdussalam Aljadania and Mostafa Elhosseini ()
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Mohammed Qaraad: Computer Science Department, Faculty of Science, Abdelmalek Essaadi University, Tetouan 93000, Morocco
Abdussalam Aljadania: Department of Management, College of Business Administration in Yanbu, Taibah University, Al-Madinah Al-Munawarah 41411, Saudi Arabia
Mostafa Elhosseini: College of Computer Science and Engineering, Taibah University, Yanbu 46421, Saudi Arabia

Mathematics, 2023, vol. 11, issue 6, 1-46

Abstract: The Competitive Swarm Optimizer (CSO) has emerged as a prominent technique for solving intricate optimization problems by updating only half of the population in each iteration. Despite its effectiveness, the CSO algorithm often exhibits a slow convergence rate and a tendency to become trapped in local optimal solutions, as is common among metaheuristic algorithms. To address these challenges, this paper proposes a hybrid approach combining the CSO with the Salp Swarm algorithm (SSA), CL-SSA, to increase the convergence rate and enhance search space exploration. The proposed approach involves a two-step process. In the first step, a pairwise competition mechanism is introduced to segregate the solutions into winners and losers. The winning population is updated through strong exploitation using the SSA algorithm. In the second step, non-winning solutions learn from the winners, achieving a balance between exploration and exploitation. The performance of the CL-SSA is evaluated on various benchmark functions, including the CEC2017 benchmark with dimensions 50 and 100, the CEC2008lsgo benchmark with dimensions 200, 500 and 1000, as well as a set of seven well-known constrained design challenges in various engineering domains defined in the CEC2020 conference. The CL-SSA is compared to other metaheuristics and advanced algorithms, and its results are analyzed through statistical tests such as the Friedman and Wilcoxon rank-sum tests. The statistical analysis demonstrates that the CL-SSA algorithm exhibits improved exploitation, exploration, and convergence patterns compared to other algorithms, including SSA and CSO, as well as popular algorithms. Furthermore, the proposed hybrid approach performs better in solving most test functions.

Keywords: global optimization; meta-heuristic; swarm intelligence; large-scale global optimization; Salp Swarm algorithm; competitive swarm; constrained mechanical and engineering design problems (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 (1)

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