Abstract
In this paper a general particle swarm optimization based on simulated annealing algorithm (SA-GPSO) for the solution to multi-specification one-dimensional cutting stock problem is proposed. Due to the limitation of its velocity-displacement search model, particle swarm optimization (PSO) has less application on discrete and combinatorial optimization problems effectively. SA-GPSO is still based on PSO mechanism, but the new updating operator is developed from crossover operator and mutation operator of genetic algorithm. In order to repair invalid particle and reduce the searching space, best fit decrease (BFD) is introduced into repairing algorithm of SA-GPSO. According to the experimental results, it is observed that the proposed algorithm is feasible to solve both sufficient one-dimensional cutting problem and insufficient one-dimensional cutting problem.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Kennedy, J., Eberhart, R.C.: Particle Swarm Optimization. In: Proceedings of IEEE International Conference on Neural Networks, Perth Australia, pp. 1942–1948. IEEE Computer Society Press, Los Alamitos (1995)
Shi, Y., Eberhart, R.C.: Parameter Selection in Particle Swarm Adaptation. In: Evolutionary Programming, vol. VII, pp. 591–600. Springer, Heidelberg (1997)
Clerc, M., Kennedy, J.: The Particle Swarm - Explosion, Stability, and Convergence in a multidimensional complex space. IEEE Transactions on Evolutionary Computation 6, 58–73 (2002)
Gradišar, M., Jesenko, J., Resinovič, G.: Optimization of Roll Cutting in Clothing Industry. Computers and Operations Research 24, 945–953 (1997)
Schilling, G., Georgiadis, M.: An Algorithm for the Determination of Optimal Cutting Patterns. Computers and Operations Research 29, 1041–1058 (2002)
Dyckhoff, H.A: Typology of Cutting and Packing Problems. European Journal of Operational Research 44, 145–159 (1990)
Eberhart, R.C., Shi, Y.: Comparison Between Genetic Algorithms and Particle Swarm Optimization. In: Porto, V.W., Waagen, D. (eds.) Evolutionary Programming VII. LNCS, vol. 1447, pp. 611–616. Springer, Heidelberg (1998)
Parsopoulos, K.E., Vrahatis, M.N.: Recent Approaches to Global Optimization Problems Through Particle Swarm Optimization. Natural Computing 1, 235–306 (2002)
Gradišar, M., Kljajić, M., Resinovič, G.: A Hybrid Approach for Optimization of One-dimensional Cutting. European Journal of Operational Research 119, 165–174 (1999)
Peiyong, L.: Optimization for Variable Inventory of One-dimensional Cutting Stock. Mechanical Science and Technology 22, 80–86 (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Li, Y., Zheng, B., Dai, Z. (2007). General Particle Swarm Optimization Based on Simulated Annealing for Multi-specification One-Dimensional Cutting Stock Problem. In: Wang, Y., Cheung, Ym., Liu, H. (eds) Computational Intelligence and Security. CIS 2006. Lecture Notes in Computer Science(), vol 4456. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74377-4_8
Download citation
DOI: https://doi.org/10.1007/978-3-540-74377-4_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74376-7
Online ISBN: 978-3-540-74377-4
eBook Packages: Computer ScienceComputer Science (R0)