A Problem-Specific Multi-objective Evolutionary Algorithm

Abstract

Multi-objective optimization problems are a kind of problems 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, 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. Then, the crowding degree scheme, as in NSGA-II, is used to select potential promising solutions in the process of iterations such that Pareto solution set has more uniform and extensive distribution. Finally, a novel multi-objective evolutionary algorithm is presented by embedding these schemes intoMOEA/D. The simulation results show the proposed algorithm is feasible and efficient.