General Particle Swarm Optimization Based on Simulated Annealing for Multi-specification One-Dimensional Cutting Stock Problem

Authors:
Xianjun Shen;Yuanxiang Li;Bojin Zheng;Zhifeng Dai
Affiliations:
Department of Computer Science, Central China Normal University, 430079 Wuhan, China and State Key Lab of Software Engineering, Wuhan University, 430072 Wuhan, China;State Key Lab of Software Engineering, Wuhan University, 430072 Wuhan, China;College of Computer Science, South-Central University for Nationalities, 430074 Wuhan, China;State Key Lab of Software Engineering, Wuhan University, 430072 Wuhan, China
Venue:
Computational Intelligence and Security
Year:
2007

Citing 6
Cited 1

Optimization of roll cutting in clothing industry

Computers and Operations Research
Recent approaches to global optimization problems through Particle Swarm Optimization

Natural Computing: an international journal
An algorithm for the determination of optimal cutting patterns

Computers and Operations Research
Parameter Selection in Particle Swarm Optimization

EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Comparison between Genetic Algorithms and Particle Swarm Optimization

EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
The particle swarm - explosion, stability, and convergence in amultidimensional complex space

IEEE Transactions on Evolutionary Computation

New Hybrid Discrete PSO for Solving Non Convex Trim Loss Problem

International Journal of Applied Evolutionary Computation

Quantified Score

Hi-index	0.00

Visualization

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.