一种改进的粒子群优化算法

针对基本粒子群算法存在着收敛速度慢、效率低、易陷入局部最优等缺陷,为了更好地平衡全局和局部搜索能力,在粒子群算法中引入收缩因子,使算法中粒子不仅向种群最优的粒子进行学习,而且向种群中比自己优秀的所有粒子学习,增加了粒子的多样性。实验结果证明,与基本蚁群算法相比,改进的粒子群算法提高了收敛速度和效率,能一定程度地避免局部最优解的产生。

An Improved Particle Swarm Optimization Algorithm

In view of the basic particle swarm optimization algorithm exits the slow speed convergence,low efficiency, and is easy to fall into the local optimum. In order to better balance the global and local search capability, the shrinkage factor is introduced into the particle swarm optimization algorithm. The particle of the population not only learn from the best particle, but also learn from all the particles in the algorithm,the diversity of particles is increased, The experimental results show that the improved particle swarm optimization algorithm can improve convergence speed and efficiency, and avoid the generation of local optimal solution comparing with the basic ant colony algorithm.