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Best neighbor-guided artificial bee colony algorithm for continuous optimization problems

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Abstract

As a relatively recent invented swarm intelligence algorithm, artificial bee colony (ABC) becomes popular and is powerful for solving the tough continuous optimization problems. However, the weak exploitation has greatly affected the performance of basic ABC algorithm. Meanwhile, keeping a proper balance between the exploration and exploitation is critical work. To tackle these problems, this paper introduces a best neighbor-guided ABC algorithm, named NABC. In NABC, the best neighbor-guided solution search strategy is proposed to equilibrate the exploration and exploitation of new algorithm during the search process. Moreover, the global neighbor search operator has displaced the original random method in the scout bee phase aiming to preserve the search experiences. The experimental studies have been tested on a set of widely used benchmark functions (including the CEC 2013 shifted and rotated problems) and one real-world application problem (the software defect prediction). Experimental results and comparison with the state-of-the-art ABC variants indicate that NABC is very competitive and outperforms the other algorithms.

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References

  • Alcalá-Fdez J, Sánchez L, García S, del Jesús MJ, Ventura S, Garrell J, Otero J, Romero C, Bacardit J, Rivas VM (2009) Keel: a software tool to assess evolutionary algorithms for data mining problems. Soft Comput 13(3):307–318

    Article  Google Scholar 

  • Awadallah MA, Al-Betar MA, Bolaji AL, Alsukhni EM, Al-Zoubi H (2018) Natural selection methods for artificial bee colony with new versions of onlooker bee. Soft Comput. https://doi.org/10.1007/s00500-018-3299-2

  • Awadallah MA, Bolaji AL, Al-Betar MA (2015) A hybrid artificial bee colony for a nurse rostering problem. Appl Soft Comput 35:726–739

    Article  Google Scholar 

  • Aydın D, Yavuz G, Stützle T (2017) Abc-x: a generalized, automatically configurable artificial bee colony framework. Swarm Intell 11(1):1–38

    Article  Google Scholar 

  • Banitalebi A, Aziz MIA, Bahar A, Aziz ZA (2015) Enhanced compact artificial bee colony. Inf Sci 298:491–511

    Article  Google Scholar 

  • Bansal JC, Sharma H, Arya K, Nagar A (2013) Memetic search in artificial bee colony algorithm. Soft Comput 17(10):1911–1928

    Article  Google Scholar 

  • Bolaji AL, Khader AT, Al-Betar MA, Awadallah MA (2013) Artificial bee colony algorithm, its variants and applications: a survey. J Theor Appl Inf Technol 47(2):434–459

    Google Scholar 

  • Črepinšek M, Liu SH, Mernik M (2013) Exploration and exploitation in evolutionary algorithms: a survey. ACM Comput Surv 45(3):35

    MATH  Google Scholar 

  • Cui L, Li G, Lin Q, Du Z, Gao W, Chen J, Lu N (2016) A novel artificial bee colony algorithm with depth-first search framework and elite-guided search equation. Inf Sci 367:1012–1044

    Article  Google Scholar 

  • DAmbros M, Lanza M, Robbes R (2012) Evaluating defect prediction approaches: a benchmark and an extensive comparison. Empir Softw Eng 17(4–5):531–577

    Article  Google Scholar 

  • dos Santos CL, Alotto P (2011) Gaussian artificial bee colony algorithm approach applied to Loney’s solenoid benchmark problem. IEEE Trans Magn 47(5):1326–1329

    Article  Google Scholar 

  • Duan HB, Xu CF, Xing ZH (2010) A hybrid artificial bee colony optimization and quantum evolutionary algorithm for continuous optimization problems. Int J Neural Syst 20(01):39–50

    Article  Google Scholar 

  • El-Abd M (2012) Generalized opposition-based artificial bee colony algorithm. In: 2012 IEEE congress on evolutionary computation. IEEE, pp 1–4

  • Gao W, Liu S (2011) Improved artificial bee colony algorithm for global optimization. Inf Process Lett 111(17):871–882

    Article  MathSciNet  MATH  Google Scholar 

  • Gao WF, Liu SY (2012) A modified artificial bee colony algorithm. Comput Oper Res 39(3):687–697

    Article  MATH  Google Scholar 

  • Gao WF, Liu SY, Huang LL (2013a) A novel artificial bee colony algorithm based on modified search equation and orthogonal learning. IEEE Trans Cybern 43(3):1011–1024

    Article  Google Scholar 

  • Gao WF, Liu SY, Huang LL (2013b) A novel artificial bee colony algorithm with powell’s method. Appl Soft Comput 13(9):3763–3775

    Article  Google Scholar 

  • Gao WF, Huang LL, Wang J, Liu SY, Qin CD (2016) Enhanced artificial bee colony algorithm through differential evolution. Appl Soft Comput 48:137–150

    Article  Google Scholar 

  • García S, Molina D, Lozano M, Herrera F (2009) A study on the use of non-parametric tests for analyzing the evolutionary algorithms behaviour: a case study on the cec2005 special session on real parameter optimization. J Heuristics 15(6):617–644

    Article  MATH  Google Scholar 

  • Kang F, Li J, Xu Q (2009) Structural inverse analysis by hybrid simplex artificial bee colony algorithms. Comput Struct 87(13):861–870

    Article  Google Scholar 

  • Kang F, Li J, Ma Z (2011) Rosenbrock artificial bee colony algorithm for accurate global optimization of numerical functions. Inf Sci 181(16):3508–3531

    Article  MathSciNet  MATH  Google Scholar 

  • Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. Technical report-tr06, Erciyes University, Engineering Faculty, Computer Engineering Department

  • Karaboga D, Akay B (2009) A comparative study of artificial bee colony algorithm. Appl Mathd Comput 214(1):108–132

    Article  MathSciNet  MATH  Google Scholar 

  • Karaboga D, Gorkemli B (2014) A quick artificial bee colony (qABC) algorithm and its performance on optimization problems. Appl Soft Comput 23:227–238

    Article  Google Scholar 

  • Karaboga D, Gorkemli B, Ozturk C, Karaboga N (2014) A comprehensive survey: artificial bee colony (ABC) algorithm and applications. Artif Intell Rev 42(1):21–57

    Article  Google Scholar 

  • Kıran MS, Fındık O (2015) A directed artificial bee colony algorithm. Appl Soft Comput 26:454–462

    Article  Google Scholar 

  • Liang J, Qu B, Suganthan P, Hernández-Dıaz AG (2013) Problem definitions and evaluation criteria for the CEC 2013 special session on real-parameter optimization. Technical Report, Zhengzhou University, Zhengzhou, China and Nanyang Technological University, Singapore, Computational Intelligence Laboratory, p 201212

  • Mernik M, Liu SH, Karaboga D, Črepinšek M (2015) On clarifying misconceptions when comparing variants of the artificial bee colony algorithm by offering a new implementation. Inf Sci 291:115–127

    Article  MathSciNet  MATH  Google Scholar 

  • Nseef SK, Abdullah S, Turky A, Kendall G (2016) An adaptive multi-population artificial bee colony algorithm for dynamic optimisation problems. Knowl Based Syst 104:14–23

    Article  Google Scholar 

  • Qin AK, Huang VL, Suganthan PN (2009) Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Trans Evolut Comput 13(2):398–417

    Article  Google Scholar 

  • Rahnamayan S, Tizhoosh HR, Salama MM (2008) Opposition-based differential evolution. IEEE Trans Evolut Comput 12(1):64–79

    Article  Google Scholar 

  • Rajasekhar A, Abraham A, Pant M (2011) Levy mutated artificial bee colony algorithm for global optimization. In: Systems, man, and cybernetics (SMC), 2011 IEEE international conference. IEEE, pp 655–662

  • Shi Y, Pun CM, Hu H, Gao H (2016) An improved artificial bee colony and its application. Knowl Based Syst 107:14–31

    Article  Google Scholar 

  • Shi X, Li Y, Li H, Guan R, Wang L, Liang Y (2010) An integrated algorithm based on artificial bee colony and particle swarm optimization. In: 2010 Sixth international conference on natural computation

  • Suganthan PN, Hansen N, Liang JJ, Deb K, Chen YP, Auger A, Tiwari S (2005) Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization. KanGAL report 2005005

  • Tran DH, Cheng MY, Cao MT (2015) Hybrid multiple objective artificial bee colony with differential evolution for the time-cost-quality tradeoff problem. Knowl Based Syst 74:176–186

    Article  Google Scholar 

  • Van den Bergh F, Engelbrecht AP (2004) A cooperative approach to particle swarm optimization. IEEE Trans Evolut Comput 8(3):225–239

    Article  Google Scholar 

  • Wang Y, Cai Z, Zhang Q (2011) Differential evolution with composite trial vector generation strategies and control parameters. IEEE Trans Evolut Comput 15(1):55–66

    Article  Google Scholar 

  • Wang H, Rahnamayan S, Sun H, Omran MG (2013a) Gaussian bare-bones differential evolution. IEEE Trans Cybern 43(2):634–647

    Article  Google Scholar 

  • Wang H, Sun H, Li C, Rahnamayan S, Pan JS (2013b) Diversity enhanced particle swarm optimization with neighborhood search. Inf Sci 223:119–135

    Article  MathSciNet  Google Scholar 

  • Wang H, Wu Z, Rahnamayan S, Sun H, Liu Y, Js P (2014) Multi-strategy ensemble artificial bee colony algorithm. Inf Sci 279:587–603

    Article  MathSciNet  MATH  Google Scholar 

  • Weyuker EJ, Ostrand TJ, Bell RM (2010) Comparing the effectiveness of several modeling methods for fault prediction. Empiric Softw Eng 15(3):277–295

    Article  Google Scholar 

  • Xiang Y, Peng Y, Zhong Y, Chen Z, Lu X, Zhong X (2014) A particle swarm inspired multi-elitist artificial bee colony algorithm for real-parameter optimization. Comput Optim Appl 57(2):493–516

    Article  MathSciNet  MATH  Google Scholar 

  • Yang X, Tang K, Yao X (2012) A learning-to-rank algorithm for constructing defect prediction models. In: Intelligent data engineering and automated learning-IDEAL 2012, Springer, pp 167–175

  • Yao X, Liu Y, Lin G (1999) Evolutionary programming made faster. IEEE Trans Evolut Comput 3(2):82–102

    Article  Google Scholar 

  • Zhao H, Pei Z, Jiang J, Guan R, Wang C, Shi X (2010) A hybrid swarm intelligent method based on genetic algorithm and artificial bee colony. In: International conference in swarm intelligence. Springer, pp 558–565

  • Zhou X, Wu Z, Wang H, Rahnamayan S (2016) Gaussian bare-bones artificial bee colony algorithm. Soft Comput 20(3):907–924

    Article  Google Scholar 

  • Zhu G, Kwong S (2010) Gbest-guided artificial bee colony algorithm for numerical function optimization. Appl Math Comput 217(7):3166–3173

    MathSciNet  MATH  Google Scholar 

Download references

Acknowledgements

This work is supported by the National Natural Science Foundation of China (No.61763019), the Science and Technology Plan Projects of Jiangxi Provincial Education Department (No.GJJ161076).

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Authors and Affiliations

  1. School of Information Science and Technology, Jiujiang University, Jiujiang, 332005, China

    Hu Peng & Changshou Deng

  2. School of Computer, Wuhan University, Wuhan, 430072, China

    Zhijian Wu

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  1. Hu Peng
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  2. Changshou Deng
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  3. Zhijian Wu
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Correspondence to Changshou Deng.

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Peng, H., Deng, C. & Wu, Z. Best neighbor-guided artificial bee colony algorithm for continuous optimization problems. Soft Comput 23, 8723–8740 (2019). https://doi.org/10.1007/s00500-018-3473-6

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