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针对基本微粒群优化(PSO,Particle Swarm Optimization)算法在应用于具有极多局部极值和维数被优化问题时易陷入局部最优和早熟收敛的不足,提出了一种新的改进算法称之为欧氏微粒群算法.此改进算法的主要思想是当算法陷入局部最优时,给微粒一个扰动因子,它的大小会因当前微粒与全局最优微粒的欧式距离的大小而自适应变化,促使微粒跳出局部最优.在实验中选取典型标准函数对算法进行测试,实验结果表明,本文算法优于标准微粒群算法(SPSO)和高斯微粒群算法(GPSO),而且随着问题复杂性的提高其性能优越性越明显.
Aiming at the shortcoming that Particle Swarm Optimization (PSO) algorithm is easy to fall into the local optimum and premature convergence when it is applied to the problem with many local extremum and dimension optimization problems, a new improved algorithm is proposed Euclidean particle swarm optimization algorithm.The main idea of this improved algorithm is that when the algorithm falls into local optimum, it gives a particle a perturbation factor, its size will be adaptively changed due to the European-style distance between the current particle and the global optimum particle, So that the particles jump out of the local optimum.The typical standard function is selected in the experiment to test the algorithm.The experimental results show that the proposed algorithm is superior to the standard particle swarm optimization (SPSO) and the Gaussian Particle Swarm Optimization (GPSO) algorithm, and with the complexity of the problem Improve its performance superiority the more obvious.