计算机应用

• 人工智能(Artificial intelligence) • 上一篇    下一篇

基于广义遗传粒子群优化算法的供应链优化求解

胡桂武   

  1. 广东商学院数学与计算科学系,广东 广州 510320
  • 收稿日期:2008-05-29 修回日期:2008-07-22 发布日期:2008-11-01 出版日期:2008-11-01
  • 通讯作者: 胡桂武

Generalized genetic particle swarm optimization for supply chain optimization

Gui-wu HU   

  • Received:2008-05-29 Revised:2008-07-22 Online:2008-11-01 Published:2008-11-01
  • Contact: Gui-wu HU

摘要: 供应链优化研究是供应链管理中的一个重要问题,也是一个难题,首先提出了一个新型供应链优化模型,针对该优化问题的求解,构造了融入特殊自然演化规则的广义遗传算法(GA),并且与粒子群优化结合,得到了广义遗传粒子群优化算法,克服了粒子群优化算法局部收敛的缺陷,提高了其全局收敛的能力。实验表明,对供应链优化问题的求解,广义遗传粒子群优化算法优于传统的遗传算法、粒子群优化算法和分枝界定法。

关键词: 遗传算法, 粒子群优化算法, 供应链管理

Abstract: Supply chain optimization is an important and difficult problem in supply chain management. Firstly, a novel supply chain optimization model was proposed in the paper. Secondly, the generalized Genetic Algorithm (GA) that embedded particular natural evaluative rules was built, and Generalized Genetic Particle Swarm Optimization (GGPSO) was gotten by combining GA and PSO. The novel method overcomes the local convergence of PSO and improves its global research ability. The experimental results show that GGPSO does better than branch and bound methods, traditional GA and PSO.

Key words: Genetic Algorithm (GA), Particle Swarm Optimization (PSO), supply chain management