China Mechanical Engineering ›› 2011, Vol. 22 ›› Issue (16): 1949-1953,1959.

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An Adaptive Ant Colony Optimization for Solving Assembly Line Balancing Problem

Deng Fuping;Zhang Chaoyong;Lian Kunlei;Xu Shaotan
  

  1. State Key Laboratory of Digital Manufacturing Equipment and Technology,Huazhong University of Science and Technology,Wuhan,430074
  • Online:2011-08-25 Published:2011-08-30
  • Supported by:
     
    Key Program of National Natural Science Foundation of China(No. 51035001);
    National Natural Science Foundation of China(No. 50875101);
    National High-tech R&D Program of China (863 Program) (No. 2007AA04Z107)

基于自适应蚁群算法的装配线平衡问题研究

邓福平;张超勇;连坤雷;徐绍锬
  

  1. 华中科技大学数字制造装备与技术国家重点实验室,武汉,430074
  • 基金资助:
    国家自然科学基金资助重点项目(51035001);国家自然科学基金资助项目(50875101);国家高技术研究发展计划(863计划)资助项目(2007AA04Z107) 
    Key Program of National Natural Science Foundation of China(No. 51035001);
    National Natural Science Foundation of China(No. 50875101);
    National High-tech R&D Program of China (863 Program) (No. 2007AA04Z107)

Abstract:

An adaptive ant colony optimization was proposed to solve
the assembly line balancing problem(ALBP).According to the characteristics of
the ALBP,a method of solution constructing strategy was developed,and a better differentiation of objective function was proposed to appraisal solution quality.However,general ant algorithm often falls into local optimal and consume excessive time, in order to overcome these shortcoming,an improved ACO was presented by adaptive adjustment of the parameters in the algorithm, which has a good ability of searching better solution at higher convergence speed. Finally, the proposed algorithm was tested and compared against best known algorithms reported in the literatures, and the experimental results indicate the feasibility and effectiveness of the proposed algorithm.

Key words: assembly line balancing, ant colony optimization(ACO), adaptive, artificial intelligence

摘要:

提出了一种自适应蚁群算法,用以求解装配线平衡问题。在该算法中,针对装配线平衡问题的具体特点,设计了一种蚂蚁分配方案可行解的构造策略,提出了一种比传统方法区分度更高的评价解质量的目标函数,同时为了克服蚁群算法易陷入局部最优和收敛速度慢等缺陷,通过自适应地调整算法的挥发度等系数,在保证收敛速度的条件下提高了解的全局性。最后,通过实例验证,证明了算法的可行性和有效性。

 

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