融合麻雀搜索算法和柯西变异策略的沙猫群优化算法

The Improved Sand Cat Swarm Optimization Algorithm Based on Multi-strategy

针对原始的沙猫群优化算法在迭代后期搜索效率低,容易陷入局部最优的问题,提出了一种多策略改进的沙猫群优化算法(ISCSO).首先,通过融合麻雀搜索算法的搜索机制策略,提高了沙猫靠近和捕获猎物的速度,有效地提升了算法后期的搜索能力.其次,为避免算法出现早熟收敛现象,引入limit阈值判断算法是否陷入局部最优.最后,采用柯西变异策略,改变个体所处位置,提高种群多样性,使算法跳出局部最优.通过在6种不同类型的基准测试函数上进行仿真,对实验结果进行数值分析,结果表明:改进后的沙猫群优化算法在求解高维复杂问题上具有精度高、收敛速度快、鲁棒性强等优势.

The improved sand cat swarm optimization(ISCSO)algorithm is proposed based on multi-strategy to solve the problem that the original sand cat swarm optimization(SCSO)has low search effi-ciency in the late iterative stage and easily falls into local optimum.Firstly,by integrating the search mechanism of the sparrow search algorithm,the speed of the sand cat approaching and capturing the prey is improved,effectively enhancing the search ability of the algorithm in the later stages.Secondly,to avoid premature convergence of the algorithm,a limit threshold is introduced to determine whether the algorithm falls into a local optimum.Finally,a Cauchy variation strategy is used to change the location of the indi-viduals and increase the population diversity so that the algorithm can jump out of the local optimum.Through simulation on six different types of benchmark test functions,numerical analysis of experimental results and convergence analysis,it shows that the improved ISCSO algorithm has the advantages of high accuracy,fast convergence,and robustness in solving high-dimensional complex problems.