Abstract
Ant Colony Algorithm is a new bionic algorithm which adopts the positive feedback structure, combines parallel computing and heuristic factors. It showed remarkable performance in combinatorial optimization problem. One-dimensional Cutting Stock is one of the classic NP-hard combinatorial optimization problems. It is widely applied in engineering technology and industrial production. Aiming at the specific characteristics of the problem, a series of improvement strategies and the specific algorithm implementation steps are given. Then the parameters are analyzed in details. Through experiment analysis and results comparison, it is proved that the improvement strategies and adjusted parameters have advantages in implementation efficiency and solving ability.
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Huang, L., Chen, H., Yang, B., Zhou, CG. (2009). Research on Improvement Strategies and Parameter Analysis of Ant Colony Algorithm for One-Dimensional Cutting Stock Problem. In: Yu, W., Sanchez, E.N. (eds) Advances in Computational Intelligence. Advances in Intelligent and Soft Computing, vol 116. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03156-4_32
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DOI: https://doi.org/10.1007/978-3-642-03156-4_32
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