Sand Cat Swarm Optimization Based on Stochastic Variation With Elite Collaboration

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