A Problem-Specific Constrained Multi-objective Evolutionary Algorithm

Abstract

Multi-objective optimization problem is a kind of problem optimizing simultaneously several conflicting objectives and keeping a balance between the diversity and the convergence of solutions. In this paper, some novel techniques are designed to improve the efficiency of multi-objective evolutionary algorithms. Firstly, fitness in keeping with feasibility is designed to choose the individual with good feasibility as much as possible; secondly, a specific sub-function is separated from a series of objectives, which is applied to provide an approximate search direction and speed the convergence of the algorithm. Thirdly, differential evolution is used to make the population search to the optimal solutions; then, the crowding degree scheme, as in NSGA-II, is used to select potential promising solutions in the process of iterations such that Pareto front is uniform as much as possible. Finally, a novel multi-objective evolutionary algorithm is presented by embedding these schemes. The simulation illustrates the effectives of the proposed algorithm.