Sand Cat Swarm Optimization Based on Stochastic Variation With Elite Collaboration

• Published in: IEEE access Vol. 10; pp. 89989 - 90003
• Main Authors: Li, Yiming, Wang, Gencheng
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Behavioral sciences; Collaboration; Convergence; elite collaboration; Heuristic algorithms; Machine learning; Metaheuristics; nonlinear periodic adjustment; Nonlinear systems; Optimization; Optimization algorithms; Particle swarm optimization; Performance enhancement; Sand; Sand cat swarm optimization; Search algorithms; Statistics; Stochastic processes; stochastic variation; Trigonometric functions
• DOI: 10.1109/ACCESS.2022.3201147
• ISSN: 2169-3536

The sand cat swarm optimization (SCSO) is a new heuristic optimization algorithm that simulates the behavior of sand cat groups in the desert using hearing to hunt. To address the shortcomings of low accuracy of SCSO solution, slow convergence in late iterations and easy convergence stagnation, a sand cat swarm optimization based on stochastic variation and elite collaboration (SE-SCSO) is proposed. SE-SCSO first introduces a nonlinear periodic adjustment mechanism to balance the exploration and local exploitation ability of the algorithm and accelerate the convergence of the algorithm.Secondly, the pseudo-opposition and pseudo-reflection learning mechanisms are used to speed up the optimization-seeking efficiency of the SCSO algorithm and improve the global convergence capability. Designing elite collaborative strategies with random variation to enable the algorithm to jump away from the local extrema, further improving the algorithm's optimization-seeking accuracy and convergence speed. In the simulation experiments, SE-SCSO is compared with Sand Cat Swarm Optimization (SCSO), Sine Cosine Algorithm (CSA), Circle Search Algorithm (SCA), Salp Swarm Algorithm (SSA), Harris Hawks Optimization (HHO), Whale Optimization Algorithm (WOA), and Golden Jackal Optimization (GJO) are tested for comparison. The experimental results validate the effectiveness of the proposed improvement strategies. Finally, SE-SCSO is applied to three engineering optimization problems. The results show that the improved strategy can effectively improve the performance performance of the algorithm, which gives SE-SCSO the advantages of high convergence accuracy, fast convergence, and the ability to jump out of local optimal solutions.