【摘要】 针对金豺算法种群初始化多样性不足、在搜索后期容易陷入局部最优的问题，对金豺优化算法作了改进。利用Cat混沌映射和精英反向学习策略初始化种群，利用单纯形法优化较差个体，改进了收敛因子，引入自适应权重的方式更新位置，引入个体记忆方法加快其收敛速度并采用高斯变异优化最优解。通过对8个基准测试函数进行仿真实验，与MFO算法、MVO算法、GWO算法、SCA算法进行比较，证明了经改进的GJO算法具有更高的求解精度和更快的收敛速度。更多还原

【Abstract】 The GJO algorithm is improved for the problems of insufficient diversity in the initialization of the population and easy to fall into the local optimum in the later stage of the search. Cat chaotic mapping and elite opposition-based learning strategy are used to initialize the population, simplex method is used to optimize the poorer individuals, the convergence factor is improved, an adaptive weighting method is introduced to update the position, individual memory method is introduced to speed up the convergence, and Gaussian variation is used to optimize the optimal solution. The improved GJO algorithm is proved to have higher solution accuracy and faster convergence speed by simulation experiments on eight benchmark test functions and comparison with other algorithms such as MFO, MVO, GWO, and SCA.更多还原