Paper Title:
Assembly Sequence Planning Utilizing Chaotic Adaptive Ant Colony Optimization Algorithm
  Abstract

The chaotic adaptive ant colony optimization algorithm (CAACO) is proposed to seek the optimal or near-optimal assembly sequences of mechanical products. Different from the general AACO algorithm, the parameter denoting the global evaporation rate of the AACO algorithm is not specified by the designers, but is generated with the chaotic operators in the optimization process. An example is used to validate the capability of the CAACO algorithm, and the results show that the robustness of the CAACO algorithm is enhanced and more ants in the ant colony can find their own optimal or near-optimal assembly sequences than those of the general AACO algorithm.

  Info
Periodical
Edited by
Zhenyu Du and Bin Liu
Pages
391-396
DOI
10.4028/www.scientific.net/AMM.26-28.391
Citation
Y. Wang, T. De, J. H. Liu, "Assembly Sequence Planning Utilizing Chaotic Adaptive Ant Colony Optimization Algorithm", Applied Mechanics and Materials, Vols. 26-28, pp. 391-396, 2010
Online since
June 2010
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