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Volume 41 Issue 10
Oct.  2015
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Journal of Beijing University of Aeronautics and Astronautics  > 2015  >  41(10): 1910-1917.
LEI Yuyao, JIANG Wenzhi, LIU Lijia, et al. Many-objective optimization based on sub-objective evolutionary algorithm[J]. Journal of Beijing University of Aeronautics and Astronautics, 2015, 41(10): 1910-1917. doi: 10.13700/j.bh.1001-5965.2014.0706(in Chinese)
Citation: LEI Yuyao, JIANG Wenzhi, LIU Lijia, et al. Many-objective optimization based on sub-objective evolutionary algorithm[J]. Journal of Beijing University of Aeronautics and Astronautics, 2015, 41(10): 1910-1917. doi: 10.13700/j.bh.1001-5965.2014.0706(in Chinese)
shu

Many-objective optimization based on sub-objective evolutionary algorithm

doi: 10.13700/j.bh.1001-5965.2014.0706

  • Department of Ordnance Science and Technology, Naval Aeronautical and Astronautical University, Yantai 264001, China

  • Received Date: 17 Nov 2014
  • Rev Recd Date: 13 Feb 2015
  • Publish Date: 20 Oct 2015
    Abstract
  • many-objective optimization is widely used in engineering area. There are some flaws to deal with many-objective optimization problem which the number of objectives exceeded three. The method which could chose proper individual solution is very crucial to solve high-dimension many-objective optimization problem. A sub-objective evolutionary algorithm (SOEA) was put forward to solve this problem. It was given in an abstract way to get the non-dominance solutions of high-dimension many-objective optimization problem. Firstly, the value of sub-objective function was sorted, and then partial Pareto non-dominance solutions of evolutional set were obtained quickly. By using the information of sorting, it could reduce the times of solution comparison in evolutional set and could get the solutions quickly. A uniform difference Minkowski distance algorithm and "k-neighbor" strategy were applied to compute fitness function. By using this method, it could improve the convergence speed to approach Pareto non-dominance solutions. Compared with the algorithms which can solve many-objective optimization problem for computing standard testing functions, it was showed the better performance of the SOEA algorithm.

     

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