The Discrete Binary Version of the Improved Particle Swarm Optimization Algorithm

For discrete variables combinatorial optimization problem, based on gene and simulated annealing algorithms thinking, the improved particle swarm algorithm is proposed in this paper. On the one hand, to improve the convergence rate, the improved algorithm combines the traditional binary particle swarm algorithm with the simulated annealing thinking to guide the evolution of the optimal solution. On the other hand, to simplify the structure of algorithm, the cross-operation of the genetic algorithm is used to replace the update operation of the speed and location. In the simulation experiment, the paper compare the binary improved particle swarm optimization (BIPSO) with the traditional binary particle swarm optimization algorithm (BPSO), the binary simulated annealing particle swarm optimization algorithm (BSAPSO), the binary cross particle swarm optimization algorithm (BCPSO) The results show that: the binary improved particle swarm algorithm ,in the convergence speed-, the global optimization capacity and the stability of algorithm convergence aspects ,is better than the other three algorithms.