Multi-objective evolutionary algorithm based on adaptive discrete Differential Evolution

In this paper, a multi-objective evolutionary algorithm based on adaptive discrete differential evolution is proposed for multi-objective optimization problems, especially in discrete domain. By introducing differential evolution to multi-objective optimization field, a novel adaptive discrete differential evolution strategy is presented firstly to enhance the ability of global exploration, so that the proposed multi-objective evolutionary algorithm can achieve the better approximate Pareto-optimal solutions. Furthermore, the proposed multi-objective evolutionary algorithm integrates the adaptive discrete differential evolution strategy with a fast Pareto ranking strategy and a truncating operation based on crowding density and Pareto rank to maintain the good diversity of evolutionary population. The simulations are conducted for a set of standard Multi-objective 0/1 knapsack problems which are the typical NP-hard problems. The performance of the proposed multi-objective evolutionary algorithm is compared with that of SPEA and NSGA-II which are state-of-the-art. Experimental results indicate that the proposed multi-objective evolutionary algorithm is more effective and efficient.