Comparison and application of four versions of particle swarm optimization algorithms in the sequence optimization

Particle swarm optimization (PSO) algorithm is a well-known optimization approach to deal with discrete problems. There are two models proposed for the operators of PSO algorithm, one is based on value exchange and the other on order exchange, accordingly two versions of PSO algorithms are formed. A new version of PSO algorithm based on order exchange has been presented in our studies, which is capable of converging on the global optimization solution, with the method of generating the stop evolution particle over again. In this paper, we propose another version of PSO algorithm based on value exchange with the same method. There exist, thus, totally four versions of PSO algorithms, which is given a brief introduction individually and the performance of which are compared in solving sequence optimization problems through fifty runs. The performance comparison show that the PSO algorithm with global convergence characteristics based on order exchange outperforms the other versions of PSO in solving sequence optimization problem.