基于汉明距离与免疫思想的粒子群算法

Particle Swarm Optimization Algorithm Based on Optimization Hamming Distance and Immune Thought

针对传统粒子群算法收敛速度慢、无法描述离散问题以及后期容易陷入局部最优解的缺陷等问题,提出一种基于汉明距离与免疫思想的改进粒子群算法(IHPSO)。首先,引入汉明距离表示位置与速度更新,使传统粒子群算法能够求解离散问题;然后,融入免疫接种、免疫选择等免疫思想,定义新的种群更新方式,解决了传统粒子群算法收敛速度慢、易陷入局部最优解的弊端;最后,通过TSP问题的模拟实验证明了改进的粒子群算法在求解速度与精度等方面均有明显提高。

An improved particle swarm optimization( IHPSO) algorithm based on hamming distance and immunity is proposed to solve the problems such as slow convergence speed of traditional particle swarm algorithm,inability to describe the properties of discrete problems and the shortcoming of local optimal solution. Firstly, the hamming distance representation position and velocity update is introduced to enable the traditional particle swarm optimization algorithm to solve discrete problems.Then,the traditional particle swarm optimization algorithm is easy to fall into the local optimal solution due to slow convergence speed. Finally,the simulation results of TSP show that the improved particle swarm optimization( pso) algorithm improves the solving speed and accuracy.