摘要
对于高维复杂函数,一般粒子群优化算法收敛速度慢,易早熟收敛。本文重构一个适合高维复杂函数惯性权重函数,使粒子群算法寻优过程中的全局收搜能力和局部收搜能力良好平衡,以达到快速收敛,高效避免早熟问题,获得最优解。对典型高维复杂函数的仿真表明:算法在求解质量和求解速度两方面都得到了好的结果。
For complex functions with high dimensions, general particle swarm optimization methods are slow speed on convergence and easy to be trapped in local optima. This paper proposes an efective particle swarm optimizer, which can automatically select a preferably inertia weight curve for the complex functions according to the fitness change ratio of swarm, and to balance global and local search ability, fasten convergence speed, avoid premature problem, and obtain global optimum. Experimental results on several benchmark complex functions with high dimensions show that the algorithm can rapidly converge at high quality solutions.
出处
《计算机科学》
CSCD
北大核心
2006年第8期202-205,共4页
Computer Science
基金
教育部科学技术重点项目(No.104262)
重庆市科委基金项目(2003-7881)共同资助
关键词
粒子群优化
惯性权重
早熟收敛问题
Particle swarm optimization, Inertia weight, Premature problem
