摘要
为了在生产中快速有效且合理地安排生产流程,达到生产最优化,采用改进粒子群权值算法(DPSO)。研究了DPSO算法地参数设置问题,在传统PSO算法基础上加入具有动态自适应调整功能的权重因子,使算法更快地达到全局最优化,迭代次数也大大缩短,将DPSO算法用于流程工业的flow-shop调度中,大大提高了生产效率,仿真实验表明该算法具有良好的全局优化性能。该成果对生产调度具有一定的参考价值和指导意义。
In order to generate a fast,efficient and rational schedule for a production process to achieve an optimal production,a modified Particle Swarm Algorithm,the weights of Particle Swarm Optimization Algorithm（DPSO）,were investigated.The study concentrated on the parameter settings for DPSO algorithm.Based on the traditional PSO algorithm,the weighting factors with dynamic and adaptive adjustment capabilities were introduced,which make the algorithm achieving global optimization faster,and also greatly reducing the number of iterations.The DPSO algorithm was used for flow-shop scheduling in process industries to greatly improve the efficiency of production.The simulation indicates that the algorithm has very good performance on global optimization.The results have a reference value to production scheduling.
出处
《辽宁工程技术大学学报（自然科学版）》
CAS
北大核心
2011年第2期308-311,共4页
Journal of Liaoning Technical University (Natural Science)
基金
国家自然科学基金资助项目(60974071)
辽宁省教育厅重点实验室基金资助项目(2009S002)
关键词
流水车间调度
粒子群优化
惯性权重
全局优化
生产调度
flow shop scheduling
particle swarm optimization
inertia weight
global optimization
production scheduling
