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粒子群算法在计算机自动配棉优化中的应用 被引量:3

Optimizing computer automatic cotton distribution using particle swarm algorithm
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摘要 为提高计算机配棉的自适应性和通用性,对于多约束条件的计算机配棉优化设计,提出了基于粒子群优化算法优化求解的方法。通过对自动配棉问题进行数学建模,将其转化为多约束条件下多维函数优化问题,再将粒子群优化算法引入模型的求解中,保证了方程组中每个可能的解都能被精确搜索到。实例证实了该方法能够快速、有效求得优化解,而且达到最优化的组合。 To improve the adaptability and versatility of computer automatic cotton distribution,an optimized solution based on particle swarm algorithm was presented for the design of computer automatic cotton distribution under multi-constraint conditions.A mathematical model is developed for automatic cotton distribution issue,transforming it into a multi-constrained and multi-dimensional function optimization problem,then the particle swarm algorithm is introduced into the solution of the model,ensuring that every possible solution in the equation groups can be accurately searched out.Practical application examples have proved that the optimal solution can be quickly and effectively reached by this method.
出处 《纺织学报》 EI CAS CSCD 北大核心 2011年第2期44-47,共4页 Journal of Textile Research
关键词 粒子群算法 自动配棉 优化 实例应用 particle swarm algorithm automatic cotton distribution optimization practical application example
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  • 1王俊伟,汪定伟.粒子群算法中惯性权重的实验与分析[J].系统工程学报,2005,20(2):194-198. 被引量:86
  • 2谭皓,沈春林,李锦.混合粒子群算法在高维复杂函数寻优中的应用[J].系统工程与电子技术,2005,27(8):1471-1474. 被引量:13
  • 3[1]Eberhart R C Kennedy J.A new optimizer using particles swarm theory.Proc Sixth International Symposium on Micro Machine and Human Science,Nagoya,Japan,1995:39-43
  • 4[2]Shi Y H,Eberhart R C.A modified particle swarm optimizer.IEEE International Conference on Evolutionary Computation,Anchorage,Alaska,May 4-9,1998:69-73
  • 5[4]Clerc M,Kennedy J.The particle swarm-explosion,stability,and convergence in a multidimensional complex space.IEEE Transactions on Evolutionary Computation,2002,6(1):58-73
  • 6Kennedy J, Eberhart R. Particle swarm optimization [A]. Proc of Int'l Conf on Neural Networks [C]. Piscataway: IEEE Press, 1995. 1942-1948.
  • 7Eberhart R, Kennedy J. A new optimizer using particle swarm theory [A]. Proc of Int'l Symposium on Micro Machine and Human Science [C]. Piscataway: IEEE Service Center, 1995. 39-43.
  • 8Shi Y, Eberhart R C. Fuzzy adaptive particle swarm optimization [A].In: Furuhashi T,Mckay B,eds. Proc Congress on Evolutionary Computation [C]. Piscataway: IEEE Press, 2001.
  • 9Lovbjerg M, Rasmussen T K, Krink T. Hybrid particle swarm optimiser with breeding and subpopulations [A]. In: Spector L,eds. Proc of Genetic and Evolutionary Computation Conference [C]. San Fransisco: Morgan Kaufmann Publishers Inc, 2001. 469-476.
  • 10Carlisle A, Dozier G. Adapting particle swarm optimization to dynamic environments [A]. In: Arabnia H R,eds. Proc of Int'l Conf on Artificial Intelligence [C]. Las Vegas: CSREA Press, 2000. 429-434.

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  • 1储才元,凌导宏.棉纤维性能和成纱质量间关系的研究[J].纺织学报,1993,14(7):4-8. 被引量:17
  • 2丁志荣.改进的方案组合配棉方法研究[J].纺织学报,2005,26(3):38-40. 被引量:10
  • 3李宁,邹彤,孙德宝,秦元庆.基于粒子群的多目标优化算法[J].计算机工程与应用,2005,41(23):43-46. 被引量:54
  • 4丁志荣.纺纱配料规则自动提取算法[J].纺织学报,2006,27(10):39-42. 被引量:1
  • 5EBERHART R C,KENNEDY J.A new optimizer using particles swarm theory[C].Proc Sixth International Symposium on Micro Machine and Human Science, 1995:87-92.
  • 6SHI Y, EBERHART R C.Empirical study of particle swarm optimization[C].Proceedings of the IEEE Congress on Evolutionary Computation, 1999 : 1945-1950.
  • 7宋水强.改进的粒子群优化算法及其在石油性质预测中的应用[D].青岛:中国石油大学,2008:15-18.
  • 8CIUPRINA G, LOAN D.Use of intelligent-particle swarm optimization in electromagnetics[J].IEEE Trans on Magnetics, 2003 , 38(2) : 1037-1040.
  • 9KASHAN A H, KARIMIB, JENABIM. A hybrid genetic heuristic for scheduling parallel batch processing, machines with arbitrary job sizes [ J]. Computers and Operations Research, 2008, 35 (4):1084- 1098.
  • 10SHI Y, EBERHART R C. A modified particle swarm optimizer[ C ]//Proceedings of the IEEE Congress on Evolutionary Computation Piscataway. N J: IEEE Press, 1998:303 - 308.

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