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.

• Periodical: Applied Mechanics and Materials (Volumes 26-28)
• Pages: 391-396
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.26-28.391
• Online since: June 2010
• Authors: Yong Wang, Tian De, Ji Hong Liu
• Keywords: Adaptive Ant Colony Optimization, Assembly Sequence Planning, Chaotic Operator

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