基于sharing函数具有死区初始化功能的 粒子群算法

主要是研究粒子群优化原理,针对粒子群算法的中局部最优问题,提出一种具有死区初始化粒子群算法.首先通过观察MATLAB可视化下粒子的运行轨迹,分析粒子陷入局部最优时的特征,并针对运行过程中出现停滞现象的粒子群,以当前局部最优粒子为中心画定“死区”,并对“死区”内的粒子重新初始化.利用标准测试函数进行测试,仿真结果表明,改进后算法不仅具有良好的稳定性,而且提高了粒子突破局部收敛限制的能力,从而提高了粒子群搜索最优解的能力.

Based on sharing function with dead-zone initialization of particle swarm optimization algorithm

An algorithm of particle swarm optimization (PSO) provided with a dead zone initialization was put forward, according to the problem of PSO in the local optimum, research on particle swarm optimization. The aim was to breakthrough the limitation of local convergence, according to the observation on the MATLAB visual particle trajectories. It analysed the characteristics of particles trapped in local optimum, and run appear stagnation phenomenon in the process of particle swarm, painting for the center of the current local optimal particle "dead-zones", and initializing "dead-zones" in the particle. The results of simulation show that the optimization precision of PSO algorithm are improved, making use of the optimal standard of test functions of the algorithm improved.