Abstract:

A particle swarm algorithm is proposed based on the gray entropy parallel analysis method to solve the multiobjective optimization problems. The gray entropy parallel analysis method combines the characteristics of the grey correlation analysis method and information entropy. The grey correlation coefficient of the data sequence is calculated, meanwhile, the information entropy and the entropy weight are also calculated, then the grey entropy parallel correlation degree is got by combining the grey relational coefficient with the entropy weight. The objective value sequence of the multiobjective optimization problem is established by the particle swarm algorithm and the number of the objective value sequence equals to the number of particles in the algorithm. The value of grey entropy parallel correlation degree of each sequence is calculated and used as the distribution strategy of the fitness value to guide the particle evolution. Ten typical job shop scheduling problems are tested by the proposed method, and the results are compared with results gained by the differential evolution algorithm and the genetic algorithm. The experimental results show that the grey entropy parallel analysis method can guide the algorithm evolution effectively with good convergence and distribution performance, and the optimization results of particle swarm algorithm are better than those of the other two algorithms.