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Gaussian bare-bones artificial bee colony algorithm

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Abstract

As a relatively new global optimization technique, artificial bee colony (ABC) algorithm becomes popular in recent years for its simplicity and effectiveness. However, there is still an inefficiency in ABC regarding its solution search equation, which is good at exploration but poor at exploitation. To overcome this drawback, a Gaussian bare-bones ABC is proposed, where a new search equation is designed based on utilizing the global best solution. Furthermore, we employ the generalized opposition-based learning strategy to generate new food sources for scout bees, which is beneficial to discover more useful information for guiding search. A comprehensive set of experiments is conducted on 23 benchmark functions and a real-world optimization problem to verify the effectiveness of the proposed approach. Some well-known ABC variants and state-of-the-art evolutionary algorithms are used for comparison. The experimental results show that the proposed approach offers higher solution quality and faster convergence speed.

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References

  • Akay B, Karaboga D (2012) A modified artificial bee colony algorithm for real-parameter optimization. Inf Sci 192:120–142

    Article  Google Scholar 

  • Alatas B (2010) Chaotic bee colony algorithms for global numerical optimization. Exp Syst Appl 37(8):5682–5687

    Article  Google Scholar 

  • Auger A, Hansen N (2005) A restart cma evolution strategy with increasing population size. Proc IEEE Congr Evolut Comput, IEEE 2:1769–1776

    Google Scholar 

  • Bäck T (1996) Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms, vol 996. Oxford University Press, Oxford

    MATH  Google Scholar 

  • Banharnsakun A, Achalakul T, Sirinaovakul B (2011) The best-so-far selection in artificial bee colony algorithm. Appl Soft Comput 11(2):2888–2901

    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 

  • Bansal JC, Sharma H, Arya K, Deep K, Pant M (2014) Self-adaptive artificial bee colony. Optimization 63(10):1513–1532

  • Beyer HG, Schwefel HP (2002) Evolution strategies—a comprehensive introduction. Nat Comput 1(1):3–52

    Article  MathSciNet  MATH  Google Scholar 

  • Brest J, Greiner S, Boskovic B, Mernik M, Zumer V (2006) Self-adapting control parameters in differential evolution: a comparative study on numerical benchmark problems. IEEE Trans Evolut Comput 10(6):646–657

    Article  Google Scholar 

  • Clerc M, Kennedy J (2002) The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Trans Evolut Comput 6(1):58–73

    Article  Google Scholar 

  • Das S, Suganthan P (2010) Problem definitions and evaluation criteria for cec 2011 competition on testing evolutionary algorithms on real world optimization problems. Jadavpur University, Nanyang Technological University, Kolkata

    Google Scholar 

  • Das S, Suganthan PN (2011) Differential evolution: a survey of the state-of-the-art. IEEE Trans Evolut Comput 15(1):4–31

    Article  Google Scholar 

  • Das S, Biswas S, Kundu S (2013) Synergizing fitness learning with proximity-based food source selection in artificial bee colony algorithm for numerical optimization. Appl Soft Comput 13(12):4676–4694

    Article  Google Scholar 

  • Dorigo M, Di Caro G (1999) Ant colony optimization: a new meta-heuristic. In: Proceedings of IEEE congress on evolutionary computation, IEEE, vol 2

  • Eberhart RC, Shi Y (2001) Particle swarm optimization: developments, applications and resources. Proc IEEE Congr Evolut Comput, IEEE 1:81–86

    Google Scholar 

  • El-Abd M (2012) Generalized opposition-based artificial bee colony algorithm. In: 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  MATH  Google Scholar 

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

    Article  MATH  Google Scholar 

  • Gao W, Liu S, Huang L (2012) A global best artificial bee colony algorithm for global optimization. J Comput Appl Math 236(11):2741–2753

    Article  MathSciNet  MATH  Google Scholar 

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

    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 CEC’2005 special session on real parameter optimization. J Heuristics 15(6):617–644

    Article  MATH  Google Scholar 

  • García S, Fernández A, Luengo J, Herrera F (2010) Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power. Inf Sci 180(10):2044–2064

  • Garro BA, Sossa H, Vázquez RA (2011) Artificial neural network synthesis by means of artificial bee colony (ABC) algorithm. In: IEEE congress on evolutionary computation, IEEE, pp 331–338

  • Gong W, Cai Z, Ling CX, Li H (2011) Enhanced differential evolution with adaptive strategies for numerical optimization. IEEE Trans Syst Man Cybern Part B: Cybern 41(2):397–413

    Article  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 repprt TR06, Erciyes University

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

    Article  MathSciNet  MATH  Google Scholar 

  • Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Glob Optim 39(3):459–471

    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 (2012a) A comprehensive survey: artificial bee colony (ABC) algorithm and applications. Artif Intell Rev 42(1):21–57

  • Karaboga D, Ozturk C, Karaboga N, Gorkemli B (2012b) Artificial bee colony programming for symbolic regression. Inf Sci 209(20):1–15

    Article  Google Scholar 

  • Kennedy J (2003) Bare bones particle swarms. In: IEEE swarm intelligence symposium, IEEE, pp 80–87

  • Kennedy J, Eberhart R (1995) Particle swarm optimization. IEEE Int Conf Neural Netw 4:1942–1948

    Article  Google Scholar 

  • Li G, Niu P, Xiao X (2012) Development and investigation of efficient artificial bee colony algorithm for numerical function optimization. Appl Soft Comput 12(1):320–332

    Article  Google Scholar 

  • Liang JJ, Qin AK, Suganthan PN, Baskar S (2006) Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans Evolut Comput 10(3):281–295

    Article  Google Scholar 

  • Mendes R, Kennedy J, Neves J (2004) The fully informed particle swarm: simpler, maybe better. IEEE Trans Evolut Comput 8(3):204–210

    Article  Google Scholar 

  • Neri F, Tirronen V (2010) Recent advances in differential evolution: a survey and experimental analysis. Artif Intell Rev 33(1):61–106

    Article  Google Scholar 

  • Qin AK, LHuang V, 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 MMA (2008) Opposition-based differential evolution. IEEE Trans Evolut Comput 12(1):64–79

    Article  Google Scholar 

  • Rajasekhar A, Abraham A, Pant M (2011a) Design of fractional order pid controller using sobol mutated artificial bee colony alogrithm. In: International conference on hybrid intelligent systems, IEEE, pp 151–156

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

  • Ratnaweera A, Halgamuge SK, Watson HC (2004) Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients. IEEE Trans Evolut Comput 8(3):240–255

    Article  Google Scholar 

  • Shang Y, Qiu Y (2006) A note on the extended Rosenbrock function. Evolut Comput 14(1):119–126

    Article  Google Scholar 

  • Sharma H, Bansal JC, Arya K (2012) Fitness based differential evolution. Memet Comput 4(4):303–316

    Article  Google Scholar 

  • Sharma H, Bansal JC, Arya K (2013) Opposition based lévy flight artificial bee colony. Memet Comput 5(3):213–227

    Article  Google Scholar 

  • Sharma TK, Pant M (2013) Enhancing the food locations in an artificial bee colony algorithm. Soft Comput 17(10):1939–1965

    Article  Google Scholar 

  • Storn R, Price K (1997) Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11(4):341–359

    Article  MathSciNet  MATH  Google Scholar 

  • 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. Technical report, Nanyang Technological University, Singapore

  • Szeto W, Wu Y, Ho SC (2011) An artificial bee colony algorithm for the capacitated vehicle routing problem. Eur J Oper Res 215(1):126–135

  • Tang KS, Man K, Kwong S, He Q (1996) Genetic algorithms and their applications. IEEE Signal Process Mag 13(6):22–37

    Article  Google Scholar 

  • Tizhoosh HR (2005) Opposition-based learning: a new scheme for machine intelligence. In: International conference on Computational intelligence for modelling, control and automation, IEEE, pp 695–701

  • TSai PW, Pan JS, Liao BY, Chu SC (2009) Enhanced artificial bee colony optimization. Int J Innov Comput Inf Control 5(12):5081–5092

    Google Scholar 

  • van den Bergh F, Engelbrecht AP (2006) A study of particle swarm optimization particle trajectories. Inf Sci 176(8):937–971

  • Wang H, Wu Z, Rahnamayan S, Liu Y, Ventresca M (2011) Enhancing particle swarm optimization using generalized opposition-based learning. Inf Sci 181(20):4699–4714

    Article  MathSciNet  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Yeh WC, Hsieh TJ (2012) Artificial bee colony algorithm-neural networks for s-system models of biochemical networks approximation. Neural Comput Appl 21(2):365–375

    Article  Google Scholar 

  • Zhan Z, Zhang J, Li Y, Shi Y (2011) Orthogonal learning particle swarm optimization. IEEE Trans Evolut Comput 15(6):832–847

    Article  Google Scholar 

  • Zhang J, Sanderson AC (2009) JADE: adaptive differential evolution with optional external archive. IEEE Trans Evolut Comput 13(5):945–958

    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

    Article  MathSciNet  MATH  Google Scholar 

Download references

Acknowledgments

This work was supported by the National Natural Science Foundation of China (Grant Nos. 61305150, 61364025, and 61462045), the Science and Technology Plan Projects of Jiangxi Provincial Education Department (Nos. GJJ13729 and GJJ14747 ), the Science and Technology Foundation of Jiangxi Province (No. 20142BAB217020), and the Humanity and Social Science Foundation of Ministry of Education of China (No. 13YJCZH174).

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Authors and Affiliations

  1. School of Computer and Information Engineering, Jiangxi Normal University, Nanchang, 330022, China

    Xinyu Zhou

  2. State Key Laboratory of Software Engineering, School of Computer, Wuhan University, Wuhan, 430072, China

    Zhijian Wu

  3. School of Information Engineering, Nanchang Institute of Technology, Nanchang, 330099, China

    Hui Wang

  4. Department of Electrical, Computer and Software Engineering, University of Ontario Institute of Technology (OUIT), 2000 Simcoe Street North, Oshawa, ON, L1H 7K4, Canada

    Shahryar Rahnamayan

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  1. Xinyu Zhou
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  2. Zhijian Wu
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  4. Shahryar Rahnamayan
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Correspondence to Xinyu Zhou.

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Communicated by V. Loia.

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Zhou, X., Wu, Z., Wang, H. et al. Gaussian bare-bones artificial bee colony algorithm. Soft Comput 20, 907–924 (2016). https://doi.org/10.1007/s00500-014-1549-5

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