Brief paper: An improved differential evolution algorithm for the task assignment problem

  • Authors:

  • Dexuan Zou;Haikuan Liu;Liqun Gao;Steven Li

  • Affiliations:

  • School of Electrical Engineering and Automation, Xuzhou Normal University, Xuzhou, Jiangsu 221116, PR China;School of Electrical Engineering and Automation, Xuzhou Normal University, Xuzhou, Jiangsu 221116, PR China;School of Information Science and Engineering, Northeastern University, Shenyang, Liaoning 110004, PR China;Division of Business University of South Australia, GPO Box 2471, Adelaide, SA 5001, Australia

  • Venue:
  • Engineering Applications of Artificial Intelligence
  • Year:
  • 2011

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Abstract

An improved differential evolution algorithm (IDE) is proposed to solve task assignment problem. The IDE is an improved version of differential evolution algorithm (DE), and it modifies two important parameters of DE algorithm: scale factor and crossover rate. Specially, scale factor is adaptively adjusted According to the objective function values of all candidate solutions, and crossover rate is dynamically adjusted with the increasement of iterations. The adaptive scale factor and dynamical crossover rate are combined to increase the diversity of candidate solutions, and to enhance the exploration capacity of solution space of the proposed algorithm. In addition, a usual penalty function method is adopted to trade-off the objective and the constraints. Experimental results demonstrate that the optimal solutions obtained by the IDE algorithm are all better than those obtained by the other two DE algorithms on solving some task assignment problems.