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一个基于PSO和DE的杂凑全局优化算法 被引量：4

Hybrid global optimization algorithm based on PSO and DE
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摘要 结合粒子群优化算法和差分进化算法思想提出了一个杂凑的全局优化算法——PSO-DE,通过对4个基准测试函数的实验测试,并与PSO和DE算法比较,证明新算法在低维(≤10维)搜索空间可以获得更高质量的解。 A hybrid global optimization algorithm,PSO-DE is presented,which is based on PSO and DE.In order to test PSO- DE,four benchmark functions are used,and the performance of the proposed PSO-DE algorithm is compared with PSO and DE, which demonstrate that it is a more effective global optimization algorithm with high solution quality in the space equal and less than 10 dimensions.
作者 王杰文 夏长清
机构地区 中南大学材料科学与工程学院 湖南第一师范学院信息技术系
出处 《计算机工程与应用》 CSCD 北大核心 2010年第3期46-48,共3页 Computer Engineering and Applications
基金 湖南第一师范学院科研项目(No.XYS0615)
关键词 粒子群优化算法 差分进化算法 杂凑算法 测试实验 particle swarm optimization differential evolution hybrid algorithm testing experiment
分类号 TP18 [自动化与计算机技术—控制理论与控制工程]
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参考文献7

  • 1Kennedy J,Eberhart R.Particle Swarm Optimization[C]//IEEE International Conference on Neural Networks(Perth,Australia).Piscataway, NJ: IEEE Service Center, 1995, Ⅳ: 1942-1948.
  • 2Shi Y H,Eberhart R C.A modified Particle Swarm Optimizer[C]// IEEE International Conference on Evolutionary Computation,Anchorage, Alaska, May 4-9,1998.
  • 3Store R,Price K.Differential evolution-A simple and efficient heuristic for global optimization over continuous spaces[J].Journal of Global Optimization, 1997,11 (4) : 341-359.
  • 4Price K,Stom R,Lampinen J.Differential evolution-A practical approach to global optimization[M].Berlin Heidelberg-New York: Springer, 2005.
  • 5李爱国.多粒子群协同优化算法[J].复旦学报（自然科学版）,2004,43(5):923-925. 被引量：398
  • 6栾丽君,谭立静,牛奔.一种基于粒子群优化算法和差分进化算法的新型混合全局优化算法[J].信息与控制,2007,36(6):708-714. 被引量：70
  • 7Das S,Abraham A,Konar A.Particle Swarm Optimization and differential evolution algorithms:Technical analysis,applications and hybridization perspectives[J].Studies in Computational Intelligence (SCI), 2008,116:1-38.

二级参考文献18

  • 1Kennedy J, Eberhart R. Particle swarm optimization [A]. Proc of Int'l Conf on Neural Networks [C]. Piscataway: IEEE Press, 1995. 1942-1948.
  • 2Eberhart 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.
  • 3Shi 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.
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  • 7Clerc M, Kennedy J. The particle swarm-explosion, stability, and convergence in a multidimensional complex space [J]. IEEE Trans on Evolutionary Computation, 2002, 6(1): 58-73.
  • 8Eberharl R, Kennedy J. A new optimizer using particle swarm theory [ A ]. Proceedings of the International Symposium on Micro Machine and Human Science [ C ]. Piscataway, N J, USA: IEEE, 1995. 39-43.
  • 9Kennedy J, Eberhart R. Particle swarm optimization [ A ]. Proceedings of the IEEE International Conference on Neural Networks [C]. Piscataway, NJ, USA: IEEE, 1995. 1942-1948.
  • 10Kennedy J, Eberhart R, Shi Y. Swarm Intelligence [ M ]. San Francisco, USA: Morgan Kaufmann Publishers, 2001.

共引文献466

同被引文献38

引证文献4

二级引证文献32

计算机工程与应用
2010年 第3期

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