基于动态因子和共享适应度的改进粒子群算法

为提高粒子群算法的收敛速度和优化性能，避免陷入局部最优，提出了一种基于动态学习因子和共享适应度函数的改进粒子群算法.在惯性权重w随着迭代次数非线性减少而动态调整学习因子的基础上，引入共享适应度函数.当算法未达到终止条件而收敛时，利用粒子和最优解间距离挑选一批粒子重新初始化形成新群体，并用共享适应度函数对新群体进行评价，新旧2个群体分别追随自己的局部最优解直至迭代结束.对4个典型多峰复杂函数的测试结果表明，该改进算法不仅加快了寻得最优解的速度，而且提高了粒子群算法全局收敛的性能.

A modified particle swarm optimization algorithm based on dynamic learning factors and sharing method

To improve the global convergence ability and rate of particle swarm optimization, an improved particle swarm optimization algorithm based on dynamic learning factors and sharing method is proposed. The inertia weight factor of the algorithm decreases non-linearly, and the learning factor changes dynamically with the descending. A sharing fitness function is introduced on the basis of dynamic regulation. When the algorithm is stagnated without reaching termination, part of the particles will be selected according to the distance between particles and optimal solution. The chosen particles will be re-initialized as a new swarm and be evaluated by sharing fitness. The old and new swarms follow their own local solutions respectively until the end of the iteration. Simulation results of four typical multimodal functions show that the modified algorithm can greatly enhance the rate of the optimal solution searching and improve the global convergence performance of PSO.