Abstract:

In the existing particle swarm optimization algorithms , the iteration of particle velocities is difficult to define for combinatorial optimization problems. In order to solve this problem , this paper proposes a general particle swarm optimization algorithm to solve the one-dimension cutting stock problem. In the proposed algorithm , the existing particle swarm optimization algorithm is combined with the genetic algorithm , the crossover operator and the
mutation operator in genetic algorithm are employed , and an adaptive strategy based on the simulated annealing algorithm is introduced to avoid the premature convergence of particle swarm. Simulated results demonstrate that the proposed algorithm is effective and robust in solving the one-dimension cutting stock problem.

Key words: general particle swarm optimization, one-dimension cutting stock problem, genetic algorithm, simulated annealing algorithm