基于选择策略的简化蝗虫优化算法

Simplified grasshopper optimization algorithm based on selection strategy

针对蝗虫优化算法(Grasshopper optimization algorithm,GOA)收敛速度慢、收敛精度不高的问题,提出基于选择策略的简化蝗虫优化算法(Simplified grasshopper optimization algorithm,SGOA)。首先运用选择策略处理初始种群,有助于快速缩小算法的搜索范围。其次通过选择策略将整个种群分为精英种群和一般种群,精英种群由当前最优蝗虫指导位置更新,有利于实现算法的趋优和加速;一般种群的位置更新取决于自身位置、精英种群及当前最优蝗虫位置,有利于保持算法的稳定。为验证SGOA求解高维复杂函数的广泛适用性,选取GOA、经典的粒子群优化算法(Particle swarm optimization,PSO)、高效的灰狼优化算法(Gray wolf optimization,GWO)以及鲸鱼优化算法(Whale optimization algorithm,WOA)作为SGOA的对比算法。以上5种算法求解9个标准测试函数的统计结果表明:SGOA的收敛精度、稳定性以及寻优成功率均显著高于其他算法。

Aiming at the problems of slow convergence speed and low convergence accuracy of grasshopper optimization algorithm(GOA),a simplified grasshopper optimization algorithm(SGOA)based on selection strategy is proposed.Firstly,the initial population is processed by selection strategy,which helps to quickly narrow the search range of the algorithm.Secondly,the whole population is divided into elite population and general population by selection strategy.The position update of elite population guided by the current optimal grasshopper is conducive to the optimization and acceleration of the algorithm;the position update of general population depends on its own position,the elite population and the current optimal grasshopper position,which is conducive to maintaining the stability of the algorithm.In order to verify the wide applicability of SGOA in solving high-dimensional complex functions,GOA,particle swarm optimization,gray wolf optimization and whale optimization algorithm are selected as the comparison algorithms for SGOA.The statistical results of the above five algorithms for solving nine standard test functions show that the convergence accuracy,stability and optimization success rate of SGOA are significantly higher than those of other algorithms.