General Particle Swarm Optimization Based on Simulated Annealing for Multi-Specification One-dimensional Cutting Stock Problem

In this paper, a general particle swarm optimization based on SA 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 simulated annealing algorithm, 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

[1]  Chuen-Lung Chen,et al.  A simulated annealing heuristic for the one-dimensional cutting stock problem , 1996 .

[2]  Miro Gradisar,et al.  Optimization of roll cutting in clothing industry , 1997, Comput. Oper. Res..

[3]  Michael N. Vrahatis,et al.  Recent approaches to global optimization problems through Particle Swarm Optimization , 2002, Natural Computing.

[4]  Maurice Clerc,et al.  The particle swarm - explosion, stability, and convergence in a multidimensional complex space , 2002, IEEE Trans. Evol. Comput..

[5]  Russell C. Eberhart,et al.  Parameter Selection in Particle Swarm Optimization , 1998, Evolutionary Programming.

[6]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[7]  A. E. Eiben,et al.  Evolutionary Programming VII , 1998, Lecture Notes in Computer Science.

[8]  Y. J. Cao,et al.  Evolutionary programming , 1997, Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97).

[9]  Michael C. Georgiadis,et al.  An algorithm for the determination of optimal cutting patterns , 2002, Comput. Oper. Res..

[10]  Harald Dyckhoff,et al.  A typology of cutting and packing problems , 1990 .

[11]  Miro Gradisar,et al.  A hybrid approach for optimization of one-dimensional cutting , 1999, Eur. J. Oper. Res..

[12]  Russell C. Eberhart,et al.  Comparison between Genetic Algorithms and Particle Swarm Optimization , 1998, Evolutionary Programming.