摘要： 针对混沌变异的小生境粒子群优化算法(NCPSO)进化中收敛速度慢、搜索精度低的缺点,提出一种基于调节因子的NCPSO改进算法(NCPSO-FLV)。通过引入速度调节因子,对收敛速度做出判断,改变粒子速度帮助粒子跳出局部最优值,使种群多样性得到加强,收敛速度和搜索精度得到提高。实验结果表明,与基于惯性权重的基本粒子群算法和NCPSO算法相比,NCPSO-FLV算法的精度更高，适用于生产任务分配的工业优化计算。
关键词:
粒子群优化算法,
混沌变异的小生境粒子群优化算法,
调节因子,
速度,
位置
Abstract: For disadvantages such as slow convergence velocity and inaccurate level of Niche Chaotic Mutation Particle Swarm Optimization(NCPSO),a new algorithm using the adjusting factor is proposed in this paper.It can evaluate the convergence velocity of swarm,and change the velocity of these particles trapped in local optima,enhancing the diversity of particles in order that the accurate level and convergence velocity of new algorithm is improved.Experimental result shows that,compared with PSO with inertia weight(PSO-ω) and NCPSO,the new algorithm NCPSO-FLV is much faster and more accurate.NCPSO-FLV is utilized for the simulating experiment of engineering production task.The simulations reveal that there is a high utilization for obtaining accurate ration of production task.
Key words:
Particle Swarm Optimization(PSO) algorithm,
Niche Chaotic Mutation PSO(NCPSO) algorithm,
adjusting factor,
velocity,
position
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