一种改进的粒子群优化算法

An improved particle swarm optimization algorithm

在粒子群优化算法的基础上,将粒子群优化算法的速度更新公式中种群最优位置用所有个体的平均值与最优粒子有限邻居个体的平均值加权求和代替;通过将种群平均适应度和整体最优位置适应度的比值作为适应度函数,并引入了加速系数;得到改进的粒子群优化聚类算法既能够充分参考当前粒子的最优信息,也参考了所有个体的最优信息和当前最优粒子有限邻居的最优信息,在进化过程中可以通过新的适应度函数自适应地调整全局搜索和局部搜索的比重对粒子的影响,对算法收敛速度影响较小的前提下较好地提高了收敛精度。最后,选取了4组具有不同分布特征的Benchmark函数作为验证函数,试验结果表明,新算法具有较好的收敛特性。

Based on the basic particle swarm optimization algorithm,the average of best particles is replaced by the weighted sum of the average of best particles and the average of the neighbor particles in the speed update formula.Using the ratio of average fitness of the whole particles and the average fitness of best particle as the fitness function,and the acceleration factor is introduced,a new adaptive PSO algorithm is obtained.The new algorithm used both the information of present particle and the information of the whole particles and the neighbors of present particle.In the process of evolution,it can adjust the global search and the local search component by the new model adaptively.So the convergence speed and precision can be improved.Experiments on 4benchmark functions demonstrate that the new algorithm is more efficient.