针对基本灰狼算法易陷入局部最优、未考虑个体自身经验等问题,本文提出一种基于Tent映射的混合灰狼优化算法(grey wolf optimization algorithm based on particle swarm optimization,简称PSO_GWO).首先,其通过Tent混沌映射产生初始种...针对基本灰狼算法易陷入局部最优、未考虑个体自身经验等问题,本文提出一种基于Tent映射的混合灰狼优化算法(grey wolf optimization algorithm based on particle swarm optimization,简称PSO_GWO).首先,其通过Tent混沌映射产生初始种群,增加种群个体的多样性;其次,采用非线性控制参数,前期递减速度慢,能够增加全局搜索能力,避免算法陷入局部最优,后期收敛因子递减速度快,增加算法局部搜索能力,从而提高整体收敛速度;最后,引入粒子群算法的思想,将个体自身经历过最优值与种群最优值相结合来更新灰狼个体的位置信息,从而保留灰狼个体自身最佳位置信息.为验证该算法的有效性,本文借助9个标准测试函数来与其他三种算法进行对比.实验结果表明,本文提出的算法比其他三种算法在单峰函数和多峰函数上搜索到的最优解更加理想; PSO_GWO算法比IGWO算法(the improved grey wolf optimization algorithm)在计算时间复杂度方面效果较好;同时,随着种群规模增大,PSO_GWO算法收敛值逐渐接近理想值.因此,本文提出的PSO_GWO算法能更快搜索到全局最优解,且鲁棒性更好.展开更多
An important problem in engineering is the unknown parameters estimation in nonlinear systems.In this paper,a novel adaptive particle swarm optimization (APSO) method is proposed to solve this problem.This work consid...An important problem in engineering is the unknown parameters estimation in nonlinear systems.In this paper,a novel adaptive particle swarm optimization (APSO) method is proposed to solve this problem.This work considers two new aspects,namely an adaptive mutation mechanism and a dynamic inertia weight into the conventional particle swarm optimization (PSO) method.These mechanisms are employed to enhance global search ability and to increase accuracy.First,three well-known benchmark functions namely Griewank,Rosenbrock and Rastrigrin are utilized to test the ability of a search algorithm for identifying the global optimum.The performance of the proposed APSO is compared with advanced algorithms such as a nonlinearly decreasing weight PSO (NDWPSO) and a real-coded genetic algorithm (GA),in terms of parameter accuracy and convergence speed.It is confirmed that the proposed APSO is more successful than other aforementioned algorithms.Finally,the feasibility of this algorithm is demonstrated through estimating the parameters of two kinds of highly nonlinear systems as the case studies.展开更多
文摘针对基本灰狼算法易陷入局部最优、未考虑个体自身经验等问题,本文提出一种基于Tent映射的混合灰狼优化算法(grey wolf optimization algorithm based on particle swarm optimization,简称PSO_GWO).首先,其通过Tent混沌映射产生初始种群,增加种群个体的多样性;其次,采用非线性控制参数,前期递减速度慢,能够增加全局搜索能力,避免算法陷入局部最优,后期收敛因子递减速度快,增加算法局部搜索能力,从而提高整体收敛速度;最后,引入粒子群算法的思想,将个体自身经历过最优值与种群最优值相结合来更新灰狼个体的位置信息,从而保留灰狼个体自身最佳位置信息.为验证该算法的有效性,本文借助9个标准测试函数来与其他三种算法进行对比.实验结果表明,本文提出的算法比其他三种算法在单峰函数和多峰函数上搜索到的最优解更加理想; PSO_GWO算法比IGWO算法(the improved grey wolf optimization algorithm)在计算时间复杂度方面效果较好;同时,随着种群规模增大,PSO_GWO算法收敛值逐渐接近理想值.因此,本文提出的PSO_GWO算法能更快搜索到全局最优解,且鲁棒性更好.
文摘An important problem in engineering is the unknown parameters estimation in nonlinear systems.In this paper,a novel adaptive particle swarm optimization (APSO) method is proposed to solve this problem.This work considers two new aspects,namely an adaptive mutation mechanism and a dynamic inertia weight into the conventional particle swarm optimization (PSO) method.These mechanisms are employed to enhance global search ability and to increase accuracy.First,three well-known benchmark functions namely Griewank,Rosenbrock and Rastrigrin are utilized to test the ability of a search algorithm for identifying the global optimum.The performance of the proposed APSO is compared with advanced algorithms such as a nonlinearly decreasing weight PSO (NDWPSO) and a real-coded genetic algorithm (GA),in terms of parameter accuracy and convergence speed.It is confirmed that the proposed APSO is more successful than other aforementioned algorithms.Finally,the feasibility of this algorithm is demonstrated through estimating the parameters of two kinds of highly nonlinear systems as the case studies.
