Abstract
In this paper we present a hybrid algorithm based on Particle Swarm Optimization (PSO) and Simulated Annealing (SA) approaches and apply it to two-dimensional non-guillotine cutting stock problem. The probability of trapping at the local optimum during the searching process can be reduced using the hybrid algorithm. Meanwhile, we propose a converting approach which is similar to the Bottom Left (BL) algorithm to map the cutting pattern to the actual layout. Finally, we implement the proposed algorithm on several test problems. The simulated results show that the performance of the hybrid algorithm is better than that of the standard PSO.
Supported by the science-technology development project of Jilin Province of China (Grant No. 20030520), the Key Science-Technology Project of the National Education Ministry of China (Grant No. 02090) and the doctoral funds of the National Education Ministry of China.
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Keywords
- Particle Swarm Optimization
- Simulated Annealing
- Hybrid Algorithm
- Standard Particle Swarm Optimization
- Cutting Pattern
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
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© 2004 Springer-Verlag Berlin Heidelberg
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Jiang, J.Q., Liang, Y.C., Shi, X.H., Lee, H.P. (2004). A Hybrid Algorithm Based on PSO and SA and Its Application for Two-Dimensional Non-guillotine Cutting Stock Problem. In: Bubak, M., van Albada, G.D., Sloot, P.M.A., Dongarra, J. (eds) Computational Science - ICCS 2004. ICCS 2004. Lecture Notes in Computer Science, vol 3037. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24687-9_98
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DOI: https://doi.org/10.1007/978-3-540-24687-9_98
Publisher Name: Springer, Berlin, Heidelberg
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