摘要
本文提出了一种新的自适应粒子群优化算法(ASPO)。该算法利用种群多样性信息对惯性权重进行非线性的调整,并在算法的后期引入速度变异算子和位置交叉算子,使算法摆脱后期易于陷入局部最优点的束缚。将其应用于函数优化问题中,仿真结果表明APSO算法能有效的解决函数优化问题。
An adaptive particle swarm optimization （APSO） algorithm was presented, In this algorithm, inertia weight was nonlinearly adjusted by using population diversity information. Velocity mutation factor and position interchange factor were both introduced and the global performance was clearly improved. The algorithm had been applied to solve function optimization problems. The simulation results had indicated that APSO was efficient to solve function optimization problems.
出处
《科技信息》
2009年第7期8-9,共2页
Science & Technology Information
基金
国家自然科学基金资助项目(60274009)。
关键词
粒子群
惯性权重
自适应
Particle swarm optimization
Inertia weight
Adaptive
