交互学习的粒子群优化算法

Interactive learning particle swarm optimization algorithm

分析基本的粒子群优化学习机制的缺陷,启发于人类社会不同群体之间可以交互学习的特点,提出了一种改进粒子群优化算法——ILPSO.在ILPSO算法中,粒子由2个种群构成.当2个种群中最佳的全局最优位置在连续一定的迭代次数内没有改善时,执行交互学习策略.依据每个种群的全局最优位置的适应值,运用模拟退火的机制和轮盘赌的方法确定学习种群和被学习种群.提出了一个基于适应度排序的经验公式,计算学习种群中的每个粒子向被学习种群学习的概率.为了摆脱选择压力,采用了一种速度变异的方法.多个测试函数的数值实验结果表明,IL-PSO具有较好的全局搜索能力,是一种求解复杂问题的有效方法.

Analyzing the drawbacks of learning mechanism in the basic particle swarm optimization（PSO）,an interactive learning particle swarm optimization（ILPSO） is presented,which is inspired by the phenomenon in human society that individuals in different groups can learn each other.Particles are composed of two populations in ILPSO.When the best particle＇s fitness value of two populations does not improve within a certain number of successive iterations,interactive learning strategies are implemented.According to the best particle′s fitness value of each population,a simulated annealing mechanism and roulette method are used to identify the learning population and the learned population.This paper proposes an empirical formula of sorting fitness value to calculate the probability of each particle in the learning population learning from the learned population.In order to escape selection pressure,a speed mutation method is used.The numerical experimental results of some benchmark functions show that ILPSO has good global search capability and is an effective method for solving complicated problems.