教与学信息交互粒子群优化算法

针对单一种群在解决高维问题中收敛速度较慢和多样性缺失的问题,提出了一种教与学信息交互粒子群优化(PSO)算法.根据进化过程将种群动态地划分为两个子种群,分别采用粒子群优化算法和教与学优化算法,同时粒子利用学习者阶段进行子种群之间信息交互,并通过评价收敛性和多样性指标让粒子的收敛能力和多样性在进化过程中得到平衡.与粒子群...

Teaching and learning information interactive particle swarm optimization algorithm

An information interactive Particle Swarm Optimization （PSO） algorithm for teaching and learning was proposed to solve high dimensional problems of low convergence rate and lack of diversity in a single population. The population was divided into two subpopulations dynamically according to evolutionary process， and processed by PSO algorithm and teaching and learning based optimization algorithm respectively. At the same time， learner stage was used by the particles to carry out information interaction between subpopulations， and by evaluating convergence and diversity indexes， the convergence ability and diversity of particles were balanced in evolutionary process. Compared with PSO algorithm， hybrid PSO and Grey Wolf Optimizer （GWO） algorithm， and improved GWO algorithm using nonlinear convergence factor and elite re-election strategy and other evolutionary algorithms in different dimensions of 15 standard test functions， the proposed algorithm can converge to the theoretical optimal value on multiple test functions， which is 1 to 6 times faster than other algorithms. Experimental results show that the proposed algorithm has good convergence accuracy and speed.