基于Iterative映射和单纯形法的改进灰狼优化算法

Improved grey wolf optimization algorithm based on iterative mapping and simplex method

为了解决基本灰狼优化算法(GWO)依赖初始种群和求解精度不高的问题,提出一种基于Iterative映射和单纯形法的改进灰狼优化算法(SMIGWO)。该算法利用混沌Iterative映射产生初始灰狼种群,增强全局搜索过程中的种群多样性;采用逆不完全Γ函数更新收敛因子,以平衡算法的全局搜索和局部搜索能力;利用单纯形法的反射、扩张和收缩操作对当前较差个体进行改进,避免算法陷入局部最优。对10个测试函数进行仿真实验,数值结果表明,与基本GWO算法、Square GWO算法、非线性收敛因子的GWO(NGWO)算法、混合GWO(HGWO)算法、粒子群优化算法(PSO)、细菌觅食算法(BFA)和引力搜索算法(GSA)相比,改进的灰狼优化算法求解精度更高,稳定性更好。

In order to solve the problem that basic Grey Wolf Optimization(GWO)algorithm depends on the initial population and has low solving precision,this paper proposed an improved grey wolf optimization algorithm based on iterative mapping and simplex method,named SMIGWO.In the proposed algorithm,firstly,chaotic iterative mapping was used to initiate individuals,which could enhance the diversity of global searching;secondly,a new updating formula of convergence factor based on inverse incompleteΓfunction was proposed to maintain a better balance between global search and local search;thirdly,reflection,expansion and compression operations of the simplex method were carried out on current poor individuals to avoid easily getting into local minima.The simulation experiments were carried out on ten benchmark functions,and the experimental results show that the SMIGWO is superior to basic GWO,SquareGWO,GWO based on Nonlinear convergence factor(NGWO),Hybrid GWO(HGWO),Partical Swarm Optimization(PSO)and Bacterial Swarm Optimization Algorithm(BFA)in computational accuracy and stability.