PENG Hu, WU Zhijian, DENG Changshou. Enhancing Differential Evolution with Commensal Learning and Uniform Local Search[J]. Chinese Journal of Electronics, 2017, 26(4): 725-733. doi: 10.1049/cje.2016.11.010
Citation: PENG Hu, WU Zhijian, DENG Changshou. Enhancing Differential Evolution with Commensal Learning and Uniform Local Search[J]. Chinese Journal of Electronics, 2017, 26(4): 725-733. doi: 10.1049/cje.2016.11.010

Enhancing Differential Evolution with Commensal Learning and Uniform Local Search

doi: 10.1049/cje.2016.11.010
Funds:  This work is supported by the National Natural Science Foundation of China (No.61364025), the Science and Technology Plan Projects of Jiangxi Provincial Education Department (No.GJJ13729), and the Science and Technology Program of Nantong (No.BK2014057).
More Information
  • Corresponding author: WU Zhijian (corresponding author) works for the State Key Laboratory of Software Engineering, School of Computer, Wuhan University, Wuhan, China. He received the B.S. degree in mathematics from Jiangxi University, China, in 1983, the M.S. degree from the Department of mathematics, Wuhan University, in 1988, and the Ph.D. degree from the School of Computing, Wuhan University, Wuhan, China, in 2004. Now he is a full professor with the State Key Laboratory of Software Engineering, Wuhan University, Wuhan, China. His current research interests include evolutionary computation, intelligent computing, parallel computing, and intelligent information processing. (Email: zhijianwu@whu.edu.cn)
  • Received Date: 2015-04-10
  • Rev Recd Date: 2015-08-20
  • Publish Date: 2017-07-10
  • Differential evolution (DE) is a popular and powerful evolutionary algorithm for global optimization problems. However, the combination of mutation strategies and parameter settings of DE is problem dependent and choosing the suitable one is a challenge work and timeconsuming. In addition, the deficiency in local exploitation also has a significant influence on the performance of DE. In order to solve these problems, a DE variant with Commensal learning and uniform local search (CUDE) has been proposed in this paper. In CUDE, commensal learning is proposed to adaptively select optimal mutation strategy and parameter setting simultaneously under the same criteria. Moreover, uniform local search enhances exploitation ability. Comprehensive experiment results on all the CEC 2013 test suite and comparison with the state-of-the-art DE variants indicate that the CUDE is very competitive.
  • loading
  • R. Storn and K. Price, “Differential evolutionCa simple and efficient heuristic for global optimization over continuous spaces”, Journal of global optimization, Vol.11, No.4, pp.341-359, 1997.
    S. Das and P.N. Suganthan, “Differential evolution: A survey of the state-of-the-art”, IEEE Transactions on Evolutionary Computation, Vol.15, No.1, pp.4-31, 2011.
    A.K. Qin, V.L. Huang and P.N. Suganthan, “Differential evolution algorithm with strategy adaptation for global numerical optimization”, IEEE Transactions on Evolutionary Computation, Vol.13, No.2, pp.398-417, 2009.
    R. Mallipeddi, P.N. Suganthan, Q.K. Pan, et al., “Differential evolution algorithm with ensemble of parameters and mutation strategies”, Applied Soft Computing, Vol.11, No.2, pp.1679-1696, 2011.
    J. Zhang and A.C. Sanderson, “JADE: Adaptive differential evolution with optional external archive”, IEEE Transactions on Evolutionary Computation, Vol.13, No.5, pp.945-958, 2009.
    H. Peng and Z. Wu, “Heterozygous differential evolution with Taguchi local search”, Soft Computing, Vol.19, No.11, pp.3273-3291, 2015.
    W. Gong, Z. Cai and C.X. Ling, “DE/BBO: A hybrid differential evolution with biogeography-based optimization for global numerical optimization”. Soft Computing, Vol.15, No.4, pp.645-665, 2010.
    N. Noman and H. Iba, “Accelerating differential evolution using an adaptive local search”, IEEE Transactions on Evolutionary Computation, Vol.12, No.1, pp.107-125, 2008.
    Y.Wang, H.X. Li, T. Huang, et al., “Differential evolution based on covariance matrix learning and bimodal distribution parameter setting”, Applied Soft Computing, Vol.18, No.5, pp.232-247, 2014.
    Y. Wang, Z. Cai and Q. Zhang, “Differential evolution with composite trial vector generation strategies and control parameters”, IEEE Transactions on Evolutionary Computation, Vol.15, No.1, pp.55-66, 2011.
    Y. Wang and K.T. Fang, “A note on uniform distribution and experimental design”, KEXUE TONGBAO, Vol.26, No.6, pp.485-489, 1981.
    K.T. Fang, D.K. Lin, P. Winker, et al., “Uniform design: Theory and application”, Technometrics, Vol.42, No.3, pp.237-248, 2000.
    L. Peng, Y. Wang, G. Dai, et al., “A novel differential evolution with uniform design for continuous global optimization”, Journal of Computers, Vol.7, No.1, pp.3-10, 2012.
    L. Peng and Y. Wang, “Differential evolution using uniformquasi-opposition for initializing the population”, Information Technology Journal, Vol.9, No.8, pp.1629-1634, 2010.
    J.J. Liang, B.Y. Qu, P.N. Suganthan, et al., “Problem definitions and evaluation criteria for the CEC 2013 special session on real-parameter optimization”, Technical Report 201212, Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou, China and Nanyang Technological University, Singapore, pp.1-39, 2013.
    Y. Wang, Z. Cai and Q. Zhang, “Enhancing the search ability of differential evolution through orthogonal crossover”, Information Sciences, Vol.185, No.1, pp.153-177, 2012.
    S. Garcia, D. Molina, M. Lozano, et al., “A study on the use of non-parametric tests for analyzing the evolutionary algorithms behaviour: A case study on the CEC 2005 special session on real parameter optimization”, Journal of Heuristics, Vol.15, No.6, pp.617-644, 2009.
    J. Alcala-Fdez, L. Sanchez, S. Garcia, et al., “KEEL: A software tool to assess evolutionary algorithms for data mining problems”, Soft Computing, Vol.13, No.3, pp.307-318, 2009.
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Article Metrics

    Article views (174) PDF downloads(357) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return