• Volume 39 Issue 3
    Mar.  2017
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    ZHANG Shui-ping, WANG Bi, CHEN Yang. Optimization for swarm intelligence based on layer-by-layer evolution[J]. Chinese Journal of Engineering, 2017, 39(3): 462-473. doi: 10.13374/j.issn2095-9389.2017.03.020
    Citation: ZHANG Shui-ping, WANG Bi, CHEN Yang. Optimization for swarm intelligence based on layer-by-layer evolution[J]. Chinese Journal of Engineering, 2017, 39(3): 462-473. doi: 10.13374/j.issn2095-9389.2017.03.020

    Optimization for swarm intelligence based on layer-by-layer evolution

    doi: 10.13374/j.issn2095-9389.2017.03.020
    • Received Date: 2016-05-09
    • A layer-by-layer evolution strategy was proposed to deal with the premature convergence of swarm intelligence as a collaborator with other existing researches based on pre-experiments and simple proofs. For promoting the precision of solution and eviting the premature convergence, the self-adaption system was constructed on the basis of the primal algorithm, operations such as compression, selection and re-initialization using the technology of layer-by-layer, and the social information was used including the compressed research space and the optimal solution. The improvements of precision of solution and the vitality of terminal individuals can be found in results of simulation experiments with benchmark functions.

       

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    • [1]
      Richardson J J, Björnmalm M, Caruso F. Technology-driven layer-by-layer assembly of nanofilms. Science, 2015, 348(6233):aaa2491-1
      [2]
      Eiben A E, Smith J. From evolutionary computation to the evolution of things. Nature, 2015, 521(7553):476
      [4]
      Eberhart R C, Shi Y. Comparing inertia weights and constriction factors in particle swarm optimization//Proceedings of the 2000 Congress on Evolutionary Computation. IEEE. La Jolla, 2000:84
      [5]
      Clerc M, Kennedy J. The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Trans Evol Comput, 2002, 6(1):58
      [6]
      Shi Y, Eberhart R. A modified particle swarm optimizer//The 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE. Anchorage, 1998:69
      [7]
      Nickabadi A, Ebadzadeh M M, Safabakhsh R. A novel particle swarm optimization algorithm with adaptive inertia weight. Appl Soft Comput, 2011, 11(4):3658
      [8]
      Zhan Z H, Zhang J, Li Y, et al. Orthogonal learning particle swarm optimization. IEEE Trans Evol Comput, 2011, 15(6):832
      [9]
      Xu G. An adaptive parameter tuning of particle swarm optimization algorithm. Appl Math Comput, 2013, 219(9):4560
      [10]
      De Oca M A M, Stützle T, Van Den Enden K, et al. Incremental social learning in particle swarms. IEEE Trans Syst Man Cybern B, 2011, 41(2):368
      [11]
      Xin B, Chen J, Zhang J, et al. Hybridizing differential evolution and particle swarm optimization to design powerful optimizers:a review and taxonomy. IEEE Trans Syst Man Cybern C, 2012, 42(5):744
      [13]
      Mendes R, Kennedy J, Neves J. The fully informed particle swarm:simpler, maybe better. IEEE Trans Evol Comput, 2004, 8(3):204
      [14]
      Zhang W J, Xie X F, Bi D C. Handling boundary constraints for numerical optimization by particle swarm flying in periodic search space//CEC2004. Congress on Evolutionary Computation. IEEE. Portland, 2004:2307
      [15]
      Helwig S, Branke J, Mostaghim S. Experimental analysis of bound handling techniques in particle swarm optimization. IEEE Trans Evol Comput, 2013, 17(2):259
      [16]
      Zhan Z H, Zhang J, Li Y,et al. Adaptive particle swarm optimization. IEEE Trans Syst Man Cybern B, 2009, 39(6):1362
      [17]
      Riget J, Vesterstrøm J S. A Diversity-guided Particle Swarm Optimizer:the ARPSO. Aarhus:Aarhus University, 2002
      [19]
      Eberhart R C, Shi Y H. Tracking and optimizing dynamic systems with particle swarms//Proceedings of the 2001 Congress on Evolutionary Computation. IEEE. Seoul, 2001:94
      [20]
      Shi Y, Eberhart R C. Empirical study of particle swarm optimization//CEC 99. Proceedings of the 1999 Congress on Evolutionary Computation. IEEE. Washington D. C., 1999
      [21]
      Chatterjee A, Siarry P. Nonlinear inertia weight variation for dynamic adaptation in particle swarm optimization. Comput Oper Res, 2006, 33(3):859
      [22]
      Li X D, Tang K, Omidvar M N, et al. Benchmark functions for the CEC 2013 special session and competition on large-scale global optimization. Gene, 2013, 7(33):8
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