摘要：

针对动态环境问题，提出一种具有自学习功能的对称粒子群算法(SymPSO)。该算法提出利用静态粒子群检测环境的变化，并基于对称粒子思想，在不增加运算量的前提下生成多个对称虚拟粒子群，扩大了种群搜索能力。为保证算法尽快逃离局部最优，给出广域学习策略，用以提高粒子的自学习能力。基于DF1环境下的仿真对比试验表明，SymPSO算法能快速跟踪最优值变化及迅速跳出局部最优，证实了其有效性。

关键词: 粒子群优化, 对称粒子群, 静态粒子, 广域学习, 动态环境

Abstract:

For the dynamic environment problem, this paper presents a self-learning function of the Symmetry Particle Swarm Optimization(SymPSO). The algorithm proposes to detect changes of the environment by using a static virtual particle swarm, and based on the thought of symmetric particles, without increasing the computational complexity, generates multiple symmetric virtual population. It can significantly expand the ability of population. To ensure the algorithm to escape from local optimum as quickly as possible, this paper proposes wide-area learning strategies to enhance self-learning ability of particles. Simulation comparative tests based on DF1 environment show that SymPSO algorithm can track the optimal value changes and escape from local optimum quickly, indicating the effectiveness of the algorithm.

Key words: Particle Swarm Optimization(PSO), Symmetry Particle Swarm Optimization(SymPSO), static particle, wide learning, dynamic environment

中图分类号: 

• TP301.6