基于免疫机制的改进粒子群优化算法研究

针对粒子群优化(PSO)算法容易陷入局部极值的不足,引入免疫机制对PSO算法进行优化,实现全局搜索。通过免疫机制的应用,根据亲和度的高低进行粒子克隆、选择、淘汰和高频变异,增强了算法全局搜索的能力,提高了收敛速度和精度。实验表明,改进后的算法完成全局搜索所需的迭代次数明显少于PSO算法,具有优良的自适应调整性能。

Study of Modified Particle Swarm Optimization Algorithm Based on Immune Principle

The immunity principle has been imported to particle swarm optimization (PSO) algorithm and applied to optimize functions, which overcomes the shortcoming of converging to local optimum for PSO algorithm. By introducing the immunity algorithm, the diversity of particle swarms and the converging rate and accuracy has been increased, and the capability of global searching has been enhanced. From the simulation results, it has been shown that the modified algorithm has perfect adaptive adjustability in multi-d...