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