摘要
自粒子群优化算法被提出以来,由于其收敛速度快、易实现,得到了快速发展和广泛应用。在此提出了一种改进型的粒子群优化算法,主要特点是随进化代数的增加而动态非线性减小惯性权重,以此改善演化后期收敛速度迅速降低的问题。为了评价其性能,选取了5个基准函数进行测试,并与惯性权重线性递减的粒子群优化算法作了比较。数字仿真表明,改进算法能极大地提高搜索性能。
Particle swarm optimization（PSO） algorithm has been developing rapidly and has been applied widely since it was proposed ,as it has rapid convergence velocity and can be easily realized. In this paper, an improved particle swarm optimization algorithm is introduced. In order to improve convergence velocity to avoid decreasing rapidly in the later period evolution ,the algorithm uses the dynamic inertia weight that non-linear decrease with iterative generation increasing. To study the performance of the algorithm, it is tested with a set＇of 5 benchmark functions and compared with the linear decrease weight particle swarm optimization algorithm. Numerical simulation results show that the improved algorithm can improve the search performance on the benchmark functions remarkably.
出处
《广西师范大学学报（自然科学版）》
CAS
北大核心
2008年第3期161-164,共4页
Journal of Guangxi Normal University:Natural Science Edition
基金
广西自然科学基金资助项目(0640067)
关键词
惯性权重
粒子群优化算法
基准函数
inertia weight
particle swarm optimization algorithm
benchmark function
