TRAN Dang Cong and WU Zhijian, “Adaptive Multi-layer Particle Swarm Optimization with Neighborhood Search,” Chinese Journal of Electronics, vol. 25, no. 6, pp. 1079-1088, 2016, doi: 10.1049/cje.2016.06.011
Citation: TRAN Dang Cong and WU Zhijian, “Adaptive Multi-layer Particle Swarm Optimization with Neighborhood Search,” Chinese Journal of Electronics, vol. 25, no. 6, pp. 1079-1088, 2016, doi: 10.1049/cje.2016.06.011

Adaptive Multi-layer Particle Swarm Optimization with Neighborhood Search

doi: 10.1049/cje.2016.06.011
Funds:  This work is supported by the National Natural Science Foundation of China (No.61070008, No.61364025), the Foundation of State Key Laboratory of Software Engineering (No.SKLSE2014-10-04), Science and Technology Program of Nantong (No.BK2014057), and Science and Technology Program of Hebei (No.12210319).
  • Received Date: 2014-08-18
  • Rev Recd Date: 2015-02-10
  • Publish Date: 2016-11-10
  • Particle swarm optimization (PSO) has shown a good performance on solving global optimization problems. Traditional PSO has two main drawbacks of premature convergence and low convergence speed, especially on complex problems. This paper presents a new approach called Adaptive multi-layer particle swarm optimization with neighborhood search (AMPSONS), where the traditional PSO is improved by employing an adaptive multi-layer search and neighborhood search strategy to achieve a trade-off between exploitation and exploration abilities. In order to evaluate the performance of the proposed AMPSONS algorithm, the performance of AMPSONS is compared with five other PSO family algorithms, namely, CLPSO, DNLPSO, DNSPSO, global MLPSO and local MLPSO on a set of benchmark functions. The comparison results show that AMPSONS has a promising performance on majority of the test functions.
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  • J. Kenedy and R. Eberhart, "Particle swarm optimization", Proceedings of IEEE International Conference on Neuron Networks Conference Proceedings, Perth Australia, pp.1942-1948, 1995.
    M. Dorigo, V. Maniezzo and A. Colorni, "The ant system: Optimization by a colony of cooperating agents", IEEE Transactions on Systems, Man and Cybernetics C Part B: Cybernetics, Vol.26, No.1, pp.29-41, 1996.
    A. Karaboga, "An idea based on honey BEE swarm for numerical optimization", Technical report TR06, Computer Engineering Department, Erciyes University, Turkey, 2005.
    S.C. Chu and P.W. Tsai, "Computational intelligence based on behaviors of cats, international journal of innovative computing", International Journal of Innovative Computing, Information and Control, Vol.3, No.1, pp.163-173, 2007.
    M. Clerc M and J. Kennedy, "The particle swarm-explosion, stability, and convergence in a multidimensional complex space", IEEE Transactions on Evolutionary Computation, Vol.6, No.1, pp.58-73, 2002.
    J. Liang, A. Qin, P. Suganthan and S. Baskarr, "Comprehensive learning particle swarm optimizer for global optimization of multimodal functions", IEEE Transactions on Evolutionary Computation, Vol.10, No.3, pp.281-295, 2006.
    H. Wang, W. Wang and Z. Wu, "Particle swarm optimization with adaptive mutation for multimodal optimization", Applied Mathematics and Computation, Vol.221, pp.296-305, 2013.
    H. Wang, Z. Wu, S. Rahnamayan, Y. Liu and M. Ventresca, "Enhancing particle swarm optimization using generalized opposition-based learning", Information Sciences, Vol.181, pp.4699-4714, 2011.
    M. Nasir and S. Das, "A dynamic neighborhood learning based particle swarm optimizer for global numerical optimization", Information Sciences, Vol.209, pp.16-36, 2012.
    H. Wang, S. Sun, C. Li, S. Rahnamayan and J. Pan, "Diversity enhanced particle swarm optimization with neighborhood search", Information Sciences, Vol.223, pp.119-135, 2013.
    D.C. Tran, Z. Wu and H. Wang, "A novel enhanced particle swarm optimization method with diversity and neighborhood search", Proceedings of IEEE International Conference on Systems, Man, and Cybernetics (SMC2013), pp.180-187, 2013.
    Y. Shi and R. Eberhart, "A modified particle swarm optimizer", Proceedings of the 1998 Congress on Evolutionary Computation (CEC98), pp.69-73, 1998.
    Y. Shi and R. Eberhart, "Empirical study of particle swarm optimization", Proceedings of the 1999 Congress on Evolutionary Computation (CEC99), pp.1945-1950, 1999.
    R. Mendes, J. Kennedy and J. Neves, "The fully informed particle swarm: Simpler, maybe better", IEEE Transactions on Evolutionary Computation, Vol.8, No.3, pp.204-210, 2004.
    L. Wang, B. Yang and Y. Chen, "Improving particle swarm optimization using multi-layer searching strategy", Information Sciences, Vol.274, pp.70-94, 2014.
    T. Hong, G. Peng, Z. Li and Y. Liang, "A novel evolutionary strategy for particle swarm optimization", Chinese Journal of Electronics, Vol.18, No.4, pp.771-774, 2009.
    S. Su and X. Cao, "Jumping PSO with expanding neighborhood search for TSP on a cuboid", Chinese Journal of Electronics, Vol.22, No.1, pp.202-208, 2013.
    X. Zhou, Z. Wu and J. Wang, "Elite opposition-based particle swarm optimization", Acta Electronica Sinica, Vol.41, No.8, pp.1647-1652, 2013. (in Chinese)
    Fei Yu, Yuanxiang Li, Bo Wei, Xing Xu and Zhiyong Zhao, "The application of a novel OBL based on lens imaging principle in PSO", Acta Electronica Sinica, Vol.42, No.2, pp.230-235, 2014. (in Chinese)
    J. Kennedy, "Small worlds and mega-minds: Effects of neighborhood topology on particle swarm performance", Proceedings of the 1999 Congress on Evolutionary Computation (CEC99), pp.1931-1938, 1999.
    J. Kennedy and R. Mendes, "Population structure and particle swarm performance", Proceedings of the 1999 Congress on Evolutionary Computation (CEC02), pp.1671-1676, 2002.
    X.D. Li, "Niching without niching parameters: particle swarm optimization using a ring topology", IEEE Transactions on Evolutionary Computation, Vol.14, No.1, pp.150-169, 2010.
    J.J. Liang and P.N. Suganthan, "Dynamic multi-swarm particle swarm optimizer", Proceedings of the Swarm Intelligence Symposium, pp.124-129, 2005.
    X. Yao, Y. Liu and G. Lin, "Evolutionary programming made faster", IEEE Transactions on Evolutionary Computation, Vol.3, No.2, pp.82-102, 1999.
    J. Brest, S. Greiner, B. Boskovic, M. Mernik and V. Zumer, "Self-adapting control parameters in differential evolution: A comparative study on numerical benchmark problems", IEEE Transactions on Evolutionary Computation, Vol.10, No.6, pp.646-657, 2006.
    P.N. Suganthan, N. Hansen and J.J. Liang, "Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization", Technical Report, Nanyang Technological University, Singapore, 2005.
    J. Derrac, "A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms", Swarm and Evolutionary Computation, Vol.1, pp.3-18, 2011.
    S. Garca and A. Fernndez, "Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power", Information Sciences, Vol.180, pp.2044-2064, 2010.
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