基于个体决策机制的粒子群算法及应用

Application of Particle Swarm Optimization with Individual Decision Mechanism

作为一种智能优化算法,粒子群算法中的粒子有不同的生活经验,因此每个粒子会做出不同的个体决策,但是这种决策机制在粒子群算法中并没有体现出来,因此本文通过引入个体决策机制的理论和方法通过个体历史适应值信息来改进粒子群算法。改进的粒子群算法应用到非线性方程组求解问题中,仿真结果表明它具有较大的优势。

As an intelligence optimization algorithm,Since each particle maintains different living experience, Thus different individual will make a different decision, However this decision mechanism cannot reflect in Particle swam optimization（PSO）,herefore with the assistant of mature individual decision way and mechanism, this paper improved PSO with individual history fitness value. The improved PSO is applied into solving nonlinear equations problem, simulation results show more superior.