Research Of A Hybrid DE Algorithm For Multi-objective Optimization Problems And Its Application In Grinding And Classification Process

Multi-objective optimization problems exist widely in natural and social sciences. The traditional optimization methods can not solve the complex problems effectively. Multi-objective evolutionary algorithms based on Pareto theory have become a hot issue for its highly efficient searching capability. Differential evolution algorithm is a newly arisen evolutionary computation technique. Due to its feasible and simple structure, strong global search ability and fast convergence speed, DE has attracted wide attention in the optimum area.Based on the intensive research of DE algtorithm and hybrid strategy with local search algorithm, the objective of this paper is to design a fast and efficient hybrid DE algorithm for solving multi-objective optimization problems, which is then applied to solve the MOPs in the grinding and classification process.In order to overcome the problems such as degradation and local optimum exist in common multi-objective DE algorithms, a novel hybrid DE algorithm for MOPs is proposed. It strengthens the exchanges of information among individuals and prevents degradation using a modified selecting method, which sorts all individuals including the parents and offspring based on the Pareto non-dominated theory. Meanwhile, the ranking index of individuals is improved to overcome the problem of uneven search. In addition, the simplex local search method is mixed in the evolution algorithm to enrich the searching behavior in the optimization process, get out from the local optimum and improve searching efficiency.Moreover, based on further research on the constraints processing mechanism of constrained multi-objective optimization problems, a hybrid DE algorithm with multi-population is designed for CMOPs. Some infeasible solutions with better performance are allowed to save and participate optimization randomly in the evolution. The advantage of the proposed algorithm is the avoidance of difficulties such as constructing penalty function and deleting meaningful infeasible solutions directly. Simulation results on benchmarks indicate that the proposed algorithm can converge quickly and effectively to the true Pareto front with better distribution.Finally, the proposed algorithm is applied to solve multi-objective optimization model of product output and quality in grinding and classification process. Based on TOPSIS, the satisfactory solution is obtained by using decision-making method on multiple attribute, achieving the goals of improving the production efficiency and maximum economic benefits.