一种改进的动态惯性权重粒子群优化算法

针对粒子群算法在寻优过程中容易陷入局部最优,以及难以平衡求精和求泛的能力,提出一种动态惯性权重粒子群优化算法。该算法同时考虑到粒子的进化速度和集聚程度对算法寻优的影响,当粒子集聚程度较高时,增大惯性权值,提高算法的全局搜索能力。为平衡算法全局和局部寻优能力,当进化速度较快时,提高局部搜索能力,以免错过较好的位置。将此算法用于优化4个经典测试函数,实验表明:此算法不仅可以平衡局部和全局的搜索能力,还能提高算法的搜索效率和精度。

A modified optimization to dynamic inertia weight particles swarm

Considering the problems of local optimum and difficulty in balancing the search capability of searching accuracy and extension caused by particle swarm optimization, this paper proposes a modified particle swarm optimization by using dynamic inertia weight. This algorithm considers the influence to opti‐mization both from the evolution velocity of particle swarm and the agglomeration degree. To improve the global searching capacity of this algorithm, and to increasethe inertia weight, when agglomeration of parti‐cles is high. In order to balance global and local optimization ability of this algorithm, local optimization a‐bility should be increased w hen algorithm has higher evolution velocity, so as not to miss a good location. The algorithm in this paper can be used in 4 classical testing functions, and the results show that the pro‐posed algorithm can not only balance the global and local search abilities, but also optimize the searching ef‐ficiency and accuracy.