基于自主学习和精英群的多子群粒子群算法

为了提高动态多子群粒子群算法中粒子学习的自主性,提出一种基于自主学习和精英群的粒子群算法.该算法借鉴教育心理学自主学习的理念,用基础群中粒子自主选择学习对象的操作代替子群的重组操作,并通过精英群局部搜索的配合来达到寻优的目的.将所提出的算法应用于6个测试函数,并与动态多子群PSO等算法进行了比较,比较结果表明,新算法在提高收敛速度、精度和寻优时间等方面具有良好的性能。

Multi-swarm particle swarm optimization based on autonomic learning and elite swarm

In order to promote the particles’ autonomic learning ability in the dynamic multi-swarm particle swarm optimization algorithm, a multi-swarm particle swarm optimization algorithm based on autonomic learning and elite swarm is proposed. According to the concept of autonomic learning , the algorithm can achieve the purpose of optimization through replacing the regrouping operation with choosing learning objects autonomously in base swarms and conducting local searches in elite swarms. The proposed algorithm is applied to six text functions and compared with the dynamic multi-swarm particle swarm optimization algorithm. The results show that the proposed algorithm has a good performance in the convergence speed, the search accuracy, the time consumption and so on.