基于适应值变化率的个体决策粒子群算法

Individual decision particle swarm optimization based on change rate of adaptive value

传统的粒子群算法通过粒子的适应值大小来判断粒子好坏,作为智能体,粒子本身有决策能力,而这在粒子群算法中并没有体现出来。因此提出了一种新的粒子好坏的判断标准——适应值变化率。通过个体决策的方法和适应值变化率,利用粒子位置与对应的适应值信息对粒子群算法中的个体历史最优位置和认知系数进行决策。采用几个常用的测试函数进行仿真实验,与其他改进的粒子群算法相比,结果表明该算法具有更好的性能。

Traditional particle swarm optimization can determine the quality of the particle by adaptive value. As an intelligent agent,each particle has the ability of decision-making,but it is not reflected in the PSO. Therefore,change rate of adaptive value,a new judgement standard for particle evaluation is proposed. The particles position and corresponding information of the adaptive value are adopted to decide individual optimal position in history and cognitive coefficient in the PSO with the help of individual decision-making method and change rate of adaptive value. Several commonly-used test functions were used in the simulation experiments. The results shows that the algorithm has a better performance than other improved PSOs.