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Volume 31 Issue 11
Nov.  2005
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Journal of Beijing University of Aeronautics and Astronautics  > 2005  >  31(11): 1232-1236.
Li Changlong, Cheng Peng, Chen Xiaobo, et al. Parallel multi-objective genetic algorithm by adding area penalty[J]. Journal of Beijing University of Aeronautics and Astronautics, 2005, 31(11): 1232-1236. (in Chinese)
Citation: Li Changlong, Cheng Peng, Chen Xiaobo, et al. Parallel multi-objective genetic algorithm by adding area penalty[J]. Journal of Beijing University of Aeronautics and Astronautics, 2005, 31(11): 1232-1236. (in Chinese)
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Parallel multi-objective genetic algorithm by adding area penalty

  • 1.

    School of Automation Science and Electrical Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100083, China

  • 2. Beijing Simulation Center, Beijing 100854, China

  • Received Date: 06 Sep 2004
  • Publish Date: 30 Nov 2005
    Abstract
  • One challenge for multi-objective genetic algorithm (MOGA) is the computational cost when MOGAs were used in the multidisciplinary optimization (MDO) problems. To improve the efficiency of MOGA, a new parallel algorithm was suggested. All the individuals were distributed among processors equally, and each processor got the extremum of Pareto solutions from all processors and constructed its own penalty function. Then each processor could divide its own Pareto solutions convergence area by the penalty function. To avoid the appearance of overlapping and omitting area and reduce the convergence time, some optimization techniques were suggested. So each processor could converge to its own special Pareto solutions segment. Because the individuals computed was divided into every processor equally, in each processor the computational cost was reduced. This with the small data changed in each processor guaranteed the efficiency. Through comparing with serial MOGA (NSGA2) and the other parallel MOGA (guided domination approach), the algorithm is proved being more effective and advanced.

     

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  • [1] Khatib W, Fleming P J. Evolutionary computing applied to MDO test problems. In:Ramana P, Grandhi V, Col L, eds. 7th AIAA/USAF/NASA /ISSMO Symposium on Multidisciplinary Analysis and Optimization. Reston:AIAA, 1998. 1980~1989 [2] Khatib W, Fleming P J. An introduction to evolutionary computing for multidisciplinary optimization[J]. Genetic Algorithms in Engineering Systems:Innovations and Applications, 1997, 1:7~12 [3] Deb K, Zope P, Jain A. Distributed computing of Pareto-optimal solutions using multi-objective evolutionary algorithm. http://www.iitk.ac.in/ kangal/pub.htm, 2002-08/2004-08 [4] Veldhuizen D A, Zydallis J B, Lamont G B. Considerations in engineering parallel multi-objective evolutionary algorithm[J]. IEEE Transacation on Evolutionary Computation,2003,7:144~173 [5] 玄光南,程润伟. 遗传算法与工程优化[M]. 北京:清华大学出版社,2004.36~40 Xuan Guangnan, Cheng Runwei. Genetic algorithm and engineering optimization[M].Beijing:Tsinghua Universty Press,2004.36~40(in Chinese) [6] Deb K, Pratap A, Agrawal S, et al. A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization:NSGA-Ⅱ[J].IEEE Transacation on Evolutionary Computation, 2002,6:182~197
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