Multi-objective evolutionary algorithm based on adaptive discrete differential evolution

  • Authors:

  • Mingming Zhang;Shuguang Zhao;Xu Wang

  • Affiliations:

  • College of Information Science and Technology, Donghua University, Shanghai, China and Engineering Research Center of Digitized Textile and Fashion Technology, Ministry of Education, Donghua Unive ...;College of Information Science and Technology, Donghua University, Shanghai, China and Engineering Research Center of Digitized Textile and Fashion Technology, Ministry of Education, Donghua Unive ...;College of Information Science and Technology, Donghua University, Shanghai, China and Engineering Research Center of Digitized Textile and Fashion Technology, Ministry of Education, Donghua Unive ...

  • Venue:
  • CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
  • Year:
  • 2009

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Abstract

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.