一种动态调整的改进微粒群算法

微粒群算法是一种新型的进化计算方法，已在许多领域得到了广泛的应用．通过对基本微粒群算法的分析，发现基本微粒群算法在计算过程中使用Lebesgue测度为0的线段进行搜索，较易得到过旱收敛现象．据此，提出了一种改进的微粒群算法，该算法在运行过程中能动态调整极限位置，从而使得每个微粒的极限位置在其所经历的最好位置与整体最好位置所形成的动态圆中分布，由于在搜索空间中使用测度为正的区域对定义域空间进行搜索，能以较大概率跳出局部最优点．实例仿真结果验证了方法的正确性和有效性．

Dynamic adjusting modified particle swarm algorithm

Particle swarm optimizer(PSO) is a new evolutionary computation method,which has been successfully applied to many fields.Through mechanism analysis of the standard particle swarm optimizer,the interval using zero Lebesque measure is used resulting premature conoergence.In terms of that a modified PSO,called dynamic adjusting modified particle swarm optimizer,is presented in the paper.It can dynamically adjust the limit position of each particle that distributes in the cycle formed by the best positions of the population and the particle,and can make the solution jump out of the local minimum point.The optimization computing of some examples is made to show that the new particle swarm optimizer is useful and simple.