A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Volume 6 Issue 2
Mar.  2019

IEEE/CAA Journal of Automatica Sinica

  • JCR Impact Factor: 11.8, Top 4% (SCI Q1)
    CiteScore: 17.6, Top 3% (Q1)
    Google Scholar h5-index: 77, TOP 5
Turn off MathJax
Article Contents
Yang Yu, Shangce Gao, Yirui Wang and Yuki Todo, "Global Optimum-Based Search Differential Evolution," IEEE/CAA J. Autom. Sinica, vol. 6, no. 2, pp. 379-394, Mar. 2019. doi: 10.1109/JAS.2019.1911378
Citation: Yang Yu, Shangce Gao, Yirui Wang and Yuki Todo, "Global Optimum-Based Search Differential Evolution," IEEE/CAA J. Autom. Sinica, vol. 6, no. 2, pp. 379-394, Mar. 2019. doi: 10.1109/JAS.2019.1911378

Global Optimum-Based Search Differential Evolution

doi: 10.1109/JAS.2019.1911378
Funds:

the JSPS KAKENHI JP17K12751

the JSPS KAKENHI JP15K00332

More Information
  • In this paper, a global optimum-based search strategy is proposed to alleviate the situation that the differential evolution (DE) usually sticks into a stagnation, especially on complex problems. It aims to reconstruct the balance between exploration and exploitation, and improve the search efficiency and solution quality of DE. The proposed method is activated by recording the number of recently consecutive unsuccessful global optimum updates. It takes the feedback from the global optimum, which makes the search strategy not only refine the current solution quality, but also have a change to find other promising space with better individuals. This search strategy is incorporated with various DE mutation strategies and DE variations. The experimental results indicate that the proposed method has remarkable performance in enhancing search efficiency and improving solution quality.

     

  • loading
  • [1]
    D. E. Goldberg and S. Voessner, "Optimizing global-local search hybrids, " in Proc. 1st Annual Conference on Genetic and Evolutionary Computation-Volume 1. Morgan Kaufmann Publishers Inc., 1999, pp. 220-228.
    [2]
    P. Moscato and C. Cotta, "A gentle introduction to memetic algorithms, " Handbook of Metaheuristics. Springer, 2003, pp. 105-144. http://lcc.uma.es/~ccottap/papers/handbook03memetic.pdf
    [3]
    N. Krasnogor and J. Smith, "A tutorial for competent memetic algorithms: model, taxonomy, and design issues, " IEEE Transactions on Evolutionary Computation, vol. 9, no. 5, pp. 474-488, 2005. doi: 10.1109/TEVC.2005.850260
    [4]
    X. Chen, Y.-S. Ong, M.-H. Lim, and K. C. Tan, "A multi-facet survey on memetic computation, " IEEE Transactions on Evolutionary Computation, vol. 15, no. 5, pp. 591-607, 2011. doi: 10.1109/TEVC.2011.2132725
    [5]
    L. Feng, Y.-S. Ong, M.-H. Lim, and I. W. Tsang, "Memetic search with interdomain learning: A realization between cvrp and carp, " IEEE Transactions on Evolutionary Computation, vol. 19, no. 5, pp. 644-658, 2015. http://ieeexplore.ieee.org/document/6920023/
    [6]
    A. Maesani, G. Iacca, and D. Floreano, "Memetic viability evolution for constrained optimization, " IEEE Transactions on Evolutionary Computation, vol. 20, no. 1, pp. 125-144, 2016. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=48120dad14aa989bfecf374355cb61b6
    [7]
    Y. Ong, M. Lim, N. Zhu, and K. Wong, "Classification of adaptive memetic algorithms: a comparative study, " Proc. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 36, no. 1, pp. 141-152, 2006. doi: 10.1109/TSMCB.2005.856143
    [8]
    P. Cowling, G. Kendall, and E. Soubeiga, "A hyperheuristic approach to scheduling a sales summit, " in International Conference on the Practice and Theory of Automated Timetabling. Springer, 2000, pp. 176-190. http://www.lifl.fr/~derbel/ueOC/cours/hyper_summit.pdf
    [9]
    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. http://www.emeraldinsight.com/servlet/linkout?suffix=b17&dbid=16&doi=10.1108%2F17563781311301535&key=10.1109%2FTEVC.2008.927706
    [10]
    Z. Song, S. Gao, Y. Yu, J. Sun, and Y. Todo, "Multiple chaos embedded gravitational search algorithm, " IEICE Transactions on Information and Systems, vol. 100, no. 4, pp. 888-900, 2017. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=J-STAGE_2199332
    [11]
    E. K. Burke, G. Kendall, and E. Soubeiga, "A tabu-search hyperheuristic for timetabling and rostering, " Journal of Heuristics, vol. 9, no. 6, pp. 451-470, 2003. doi: 10.1023/B:HEUR.0000012446.94732.b6
    [12]
    N. Krasnogor and J. Smith, "Multimeme algorithms for the structure prediction and structure comparison of proteins, " in GECCO, 2002, pp. 42-44. https://www.researchgate.net/publication/2495520_Multimeme_Algorithms_for_the_Structure_Prediction_and_Structure_Comparison_of_Proteins
    [13]
    N. Krasnogor, "Studies on the theory and design space of memetic algorithms. " Ph. D. dissertation, University of the West of England at Bristol, 2002. http://europepmc.org/theses/ETH/249135
    [14]
    J. Smith, "Co-evolving memetic algorithms: Initial investigations, " in Proc. International Conference on Parallel Problem Solving from Nature. Springer, 2002, pp. 537-546. https://www.researchgate.net/publication/220702057_Co-evolving_Memetic_Algorithms_Initial_Investigations
    [15]
    J. Smith, "Co-evolving memetic algorithms: A learning approach to robust scalable optimisation, " in Proc. IEEE Congresson Evolutionary Computation (CEC), vol. 1. IEEE, 2003, pp. 498-505. https://www.researchgate.net/publication/4074803_Co-evolving_memetic_algorithms_A_learning_approach_to_robust_scalable_optimization
    [16]
    S. Gao, C. Vairappan, Y. Wang, Q. Cao, and Z. Tang, "Gravitational search algorithm combined with chaos for unconstrained numerical optimization, " Applied Mathematics and Computation, vol. 231, pp. 48-62, 2014. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=efb8617ef0ebdad62e5e94f52b1451db
    [17]
    N. Noman and H. Iba, "Enhancing differential evolution performance with local search for high dimensional function optimization, " in Proc. of the 7th annual conference on Genetic and evolutionary computation. ACM, 2005, pp. 967-974. https://www.cs.york.ac.uk/rts/docs/GECCO_2005/Conference%20proceedings/docs/p967.pdf
    [18]
    R. Storn and K. Price, "Differential evolution--a simple and efficient heuristic for global optimization over continuous spaces, " Journal of Global Optimization, vol. 11, no. 4, pp. 341-359, 1997. doi: 10.1023/A:1008202821328
    [19]
    N. Awad, M. Ali, J. Liang, B. Qu, and P. Suganthan, "Problem definitions and evaluation criteria for the CEC 2017 special session and competition on single objective real-parameter numerical optimization, " Technical Report. NTU, Singapore, 2016.
    [20]
    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. http://ieeexplore.ieee.org/document/5601760/
    [21]
    J. Brest, S. Greiner, B. Boskovic, M. Mernik, and V. Zumer, "Selfadapting 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. doi: 10.1109/TEVC.2006.872133
    [22]
    J. Sun, S. Gao, H. Dai, J. Cheng, M. Zhou, and J. Wang, "Bi-objective elite differential evolution for multivalued logic networks, " IEEE Transactions on Cybernetics, 2018, doi: 10.1109/TCYB.2018.2868493.
    [23]
    J. Wang, W. Zhang, and J. Zhang, "Cooperative differential evolution with multiple populations for multiobjective optimization, " IEEE Transactions on Cybernetics, vol. 46, no. 12, pp. 2848-2861, 2016. doi: 10.1109/TCYB.2015.2490669
    [24]
    Y. Cai, G. Sun, T. Wang, H. Tian, Y. Chen, and J. Wang, "Neighborhoodadaptive differential evolution for global numerical optimization, " Applied Soft Computing, vol. 59, pp. 659-706, 2017. doi: 10.1016/j.asoc.2017.06.002
    [25]
    F. Neri and V. Tirronen, "Recent advances in differential evolution: a survey and experimental analysis, " Artificial Intelligence Review, vol. 33, no. 1-2, pp. 61-106, 2010. http://dl.acm.org/citation.cfm?id=1713731
    [26]
    S. Das, A. Abraham, U. K. Chakraborty, and A. Konar, "Differential evolution using a neighborhood-based mutation operator, " IEEE Transactions on Evolutionary Computation, vol. 13, no. 3, pp. 526-553, 2009. http://d.old.wanfangdata.com.cn/NSTLQK/NSTL_QKJJ0224536810/
    [27]
    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. http://d.old.wanfangdata.com.cn/NSTLQK/NSTL_QKJJ0213692140/
    [28]
    A. Caponio, F. Neri, and V. Tirronen, "Super-fit control adaptation in memetic differential evolution frameworks, " Soft Computing, vol. 13, no. 8-9, pp. 811, 2009. doi: 10.1007/s00500-008-0357-1
    [29]
    F. Neri and V. Tirronen, "Scale factor local search in differential evolution, " Memetic Computing, vol. 1, no. 2, pp. 153-171, 2009. doi: 10.1007/s12293-009-0008-9
    [30]
    D. Jia, G. Zheng, and M. K. Khan, "An effective memetic differential evolution algorithm based on chaotic local search, " Information Sciences, vol. 181, no. 15, pp. 3175-3187, 2011. doi: 10.1016/j.ins.2011.03.018
    [31]
    N. R. Sabar, J. Abawajy, and J. Yearwood, "Heterogeneous cooperative co-evolution memetic differential evolution algorithm for big data optimization problems, " IEEE Transactions on Evolutionary Computation, vol. 21, no. 2, pp. 315-327, 2017. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=df4587287810554196508078408ce1e9
    [32]
    H. Rosenbrock, "An automatic method for finding the greatest or least value of a function, " The Computer Journal, vol. 3, no. 3, pp. 175-184, 1960. http://d.old.wanfangdata.com.cn/OAPaper/oai_doaj-articles_0acaf0b4f2ae0d4cca4ea51b3bf8c280
    [33]
    M. J. Powell, "An efficient method for finding the minimum of a function of several variables without calculating derivatives, " The Computer Journal, vol. 7, no. 2, pp. 155-162, 1964. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=HighWire000002550018
    [34]
    S.-M. Guo, C.-C. Yang, P.-H. Hsu, and J. S.-H. Tsai, "Improving differential evolution with a successful-parent-selecting framework, " IEEE Transactions on Evolutionary Computation, vol. 19, no. 5, pp. 717-730, 2015. doi: 10.1109/TEVC.2014.2375933
    [35]
    R. Mallipeddi, P. N. Suganthan, Q. Pan, and M. F. Tasgetiren, "Differential evolution algorithm with ensemble of parameters and mutation strategies, " Applied Soft Computing, vol. 11, no. 2, pp. 1679-1696, 2011. doi: 10.1016/j.asoc.2010.04.024
    [36]
    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. doi: 10.1109/TEVC.2009.2014613
    [37]
    R. Tanabe and A. Fukunaga, "Success-history based parameter adaptation for differential evolution, " in Proc. IEEE Congress Evolutionary Computation (CEC). 2013, pp. 71-78. http://metahack.org/CEC2013-SHADE.pdf
    [38]
    S. Garcia, A. Fernandez, J. Luengo, and F. Herrera, "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, no. 10, pp. 2044-2064, 2010. doi: 10.1016/j.ins.2009.12.010
    [39]
    J. Derrac, S. Garcia, D. Molina, and F. Herrera, "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, no. 1, pp. 3-18, 2011. http://www.sciencedirect.com/science/article/pii/S2210650211000034
    [40]
    S. Gao, M. Zhou, Y. Wang, J. Cheng, H. Yachi, and J. Wang, "Dendritic neural model with effective learning algorithms for classification, approximation, and prediction, " IEEE Transactions on Neural Networks and Learning Systems. vol. 30, no. 2, pp. 601-614, 2019. http://ieeexplore.ieee.org/document/8409490/
    [41]
    J. Luengo, S. Garcia, and F. Herrera, "A study on the use of statistical tests for experimentation with neural networks: Analysis of parametric test conditions and non-parametric tests, " Expert Systems with Applications, vol. 36, no. 4, pp. 7798-7808, 2009. doi: 10.1016/j.eswa.2008.11.041
    [42]
    S. Gao, Y. Wang, J. Wang, and J. Cheng, "Understanding differential evolution: A Poisson law derived from population interaction network, " Journal of Computational Science, vol. 21, pp. 140-149, 2017. doi: 10.1016/j.jocs.2017.06.007
    [43]
    D. Molina, M. Lozano, C. Garcia-Martinez, and F. Herrera, "Memetic algorithms for continuous optimisation based on local search chains, " Evolutionary Computation, vol. 18, no. 1, pp. 27-63, 2010. https://ieeexplore.ieee.org/document/6793771
    [44]
    S. Mirjalili, S. M. Mirjalili, and A. Lewis, "Grey wolf optimizer, " Advances in Engineering Software, vol. 69, pp. 46-61, 2014. doi: 10.1016/j.advengsoft.2013.12.007
    [45]
    J. Cheng, J. Cheng, M. Zhou, F. Liu, S. Gao, and C. Liu, "Routing in internet of vehicles: A review, " IEEE Transactions on Intelligent Transportation Systems, vol. 16, no. 5, pp. 2339-2352, 2015. doi: 10.1109/TITS.2015.2423667
    [46]
    J. Cheng, H. Mi, Z. Huang, S. Gao, D. Zang, and C. Liu, "Connectivity modeling and analysis for internet of vehicles in urban road scene, " IEEE Access, vol. 6, pp. 2692-2702, 2018. doi: 10.1109/ACCESS.2017.2784845
    [47]
    S. Gao, Y. Wang, J. Cheng, Y. Inazumi, and Z. Tang, "Ant colony optimization with clustering for solving the dynamic location routing problem, " Applied Mathematics and Computation, vol. 285, pp. 149-173, 2016. doi: 10.1016/j.amc.2016.03.035
    [48]
    S. Wang, Aorigele, G. Liu, and S. Gao, "A hybrid discrete imperialist competition algorithm for fuzzy job-shop scheduling problems, " IEEE Access, vol. 4, pp. 9320-9331, 2016. doi: 10.1109/ACCESS.2016.2645818
    [49]
    C. Liu, J. Zhang, G. Li, S. Gao, and Q. Zeng, "A two-layered framework for the discovery of software behavior: A case study, " IEICE Transactions on Information and Systems, vol. 101, no. 8, pp. 2005-2014, 2018.
    [50]
    S. Song, S. Gao, X. Chen, D. Jia, X. Qian, and Y. Todo, "AIMOES: Archive information assisted multi-objective evolutionary strategy for ab initio protein structure prediction, " Knowledge-Based Systems, vol. 146, pp. 58-72, 2018. doi: 10.1016/j.knosys.2018.01.028
    [51]
    S. Gao, S. Song, J. Cheng, Y. Todo, and M. Zhou, "Incorporation of solvent effect into multi-objective evolutionary algorithm for improved protein structure prediction, " IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 15, no. 4, pp. 1365-1378, 2018. doi: 10.1109/TCBB.2017.2705094

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

    Figures(7)  / Tables(10)

    Article Metrics

    Article views (1717) PDF downloads(108) Cited by()

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return