Skip to main content
SpringerLink
Log in

Memetic search in artificial bee colony algorithm

  • Methodologies and Application
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

Artificial bee colony (ABC) optimization algorithm is relatively a simple and recent population based probabilistic approach for global optimization. ABC has been outperformed over some Nature Inspired Algorithms (NIAs) when tested over benchmark as well as real world optimization problems. The solution search equation of ABC is significantly influenced by a random quantity which helps in exploration at the cost of exploitation of the search space. In the solution search equation of ABC, there is a enough chance to skip the true solution due to large step size. In order to balance between diversity and convergence capability of the ABC, a new local search phase is integrated with the basic ABC to exploit the search space identified by the best individual in the swarm. In the proposed phase, ABC works as a local search algorithm in which, the step size that is required to update the best solution, is controlled by Golden Section Search approach. The proposed strategy is named as Memetic ABC (MeABC). In MeABC, new solutions are generated around the best solution and it helps to enhance the exploitation capability of ABC. MeABC is established as a modified ABC algorithm through experiments over 20 test problems of different complexities and 4 well known engineering optimization problems.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  • Akay B, Karaboga D (2010) A modified artificial bee colony algorithm for real-parameter optimization. Inf Sci. doi:10.1016/j.ins.2010.07.015

  • Ali MM, Khompatraporn C, Zabinsky ZB (2005) A numerical evaluation of several stochastic algorithms on selected continuous global optimization test problems. J Global Optim 31(4):635–672

    Article  MathSciNet  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 

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

    Article  MathSciNet  MATH  Google Scholar 

  • Brest J, Zumer V, Maucec MS (2006) Self-adaptive differential evolution algorithm in constrained real-parameter optimization. In: IEEE Congress on Evolutionary Computation 2006. CEC 2006. IEEE, pp 215–222

  • Caponio A, Cascella GL, Neri F, Salvatore N, Sumner M (2007) A fast adaptive memetic algorithm for online and offline control design of pmsm drives. Syst Man Cybernet Part B: Cybernet IEEE Trans 37(1):28–41

    Article  Google Scholar 

  • Caponio A, Neri F, Tirronen V (2009) Super-fit control adaptation in memetic differential evolution frameworks. Soft Comput-A Fusion Found, Methodol Appl 13(8):811–831

    Article  Google Scholar 

  • Chen X, Ong YS, Lim MH, Tan KC (2011) A multi-facet survey on memetic computation. IEEE Trans Evol Comput 15(5):591–607

    Article  Google Scholar 

  • Clerc M (2012) List based pso for real problems. http://clerc.maurice.free.fr/pso/ListBasedPSO/ListBasedPSO28PSOsite29.pdf, 16 July 2012

  • Cotta C, Neri F (2012) Memetic algorithms in continuous optimization. Handbook of Memetic Algorithms, pp 121–134

  • Das S, Suganthan PN (2010) Problem definitions and evaluation criteria for CEC 2011 competition on testing evolutionary algorithms on real world optimization problems. Jadavpur University, Kolkata, India, and Nangyang Technological University, Singapore, Tech. Rep, 2010

  • Dasgupta D (2006) Advances in artificial immune systems. Comput Intell Mag IEEE 1(4):40–49

    Google Scholar 

  • Diwold K, Aderhold A, Scheidler A, Middendorf M (2011) Performance evaluation of artificial bee colony optimization and new selection schemes. Memet Comput 3(3):149–162

    Article  Google Scholar 

  • Dorigo M, Di Caro G (1999) Ant colony optimization: a new meta-heuristic. In: Evolutionary Computation, 1999. CEC 99. Proceedings of the 1999 Congress on, vol 2. IEEE

  • Eiben AE, Smith JE (2003) Introduction to evolutionary computing. Springer, Belin

  • El-Abd M (2011) Performance assessment of foraging algorithms vs. evolutionary algorithms. Inf Sci 182(1):243–263

    Article  MathSciNet  Google Scholar 

  • Fister I, Fister Jr I, Brest J, Zumer V (2012) Memetic artificial bee colony algorithm for large-scale global optimization. Arxiv preprint arXiv:1206.1074

  • Fogel DB, Michalewicz Z (1997) Handbook of evolutionary computation. Taylor & Francis, London

  • Gallo C, Carballido J, Ponzoni I (2009) Bihea: a hybrid evolutionary approach for microarray biclustering. In: Advances in Bioinformatics and Computational Biology, LNCS, vol 5676. Springer, Heidelberg, pp 36–47

    Article  Google Scholar 

  • Goh CK, Ong YS, Tan KC (2009) Multi-objective memetic algorithms, vol. 171. Springer, Berlin

  • Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Addison-Wesley, Reading, MA

  • Hooke R, Jeeves TA (1961) “Direct search” solution of numerical and statistical problems. J ACM (JACM) 8(2):212–229

    Article  MATH  Google Scholar 

  • Hoos, HH Stützle T (2005) Stochastic local search: Foundations and applications. Morgan Kaufmann

  • Iacca G, Neri F, Mininno E, Ong YS, Lim MH (2012) Ockham’s razor in memetic computing: three stage optimal memetic exploration. Inf Sci: Int J 188:17–43

    Article  MathSciNet  Google Scholar 

  • Ishibuchi H, Yamamoto T (2004) Fuzzy rule selection by multi-objective genetic local search algorithms and rule evaluation measures in data mining. Fuzzy Sets Syst 141(1):59–88

    Article  MathSciNet  MATH  Google Scholar 

  • Ishibuchi H, Yoshida T, Murata T (2003) Balance between genetic search and local search in memetic algorithms for multiobjective permutation flowshop scheduling. IEEE Trans Evol Comput 7(2):204–223

    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 

  • Kang F, Li J, Ma Z, Li H (2011) Artificial bee colony algorithm with local search for numerical optimization. J Softw 6(3):490–497

    Google Scholar 

  • Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. Technical Report. TR06, Erciyes University Press, Erciyes

  • 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, Akay B (2010) A modified artificial bee colony (abc) algorithm for constrained optimization problems. Appl Soft Comput

  • Kennedy J (2006) Swarm intelligence. Handbook of Nature-Inspired and Innovative Computing, pp 187–219

  • Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Neural Networks, 1995. Proceedings, IEEE International Conference on, vol. 4. IEEE, pp 1942–1948

  • Kiefer J (1953) Sequential minimax search for a maximum. In: Proceedings of American Mathematical Society, vol. 4, pp 502–506

  • Knowles J, Corne D, Deb K (2008) Multiobjective problem solving from nature: From concepts to applications (Natural computing series). Springer, Berlin

  • Mahdavi M, Fesanghary M, Damangir E (2007) An improved harmony search algorithm for solving optimization problems. Appl Math Comput 188(2):1567–1579

    Article  MathSciNet  MATH  Google Scholar 

  • Mezura-Montes E, Velez-Koeppel RE (2010) Elitist artificial bee colony for constrained real-parameter optimization. In 2010 Congress on Evolutionary Computation (CEC2010), IEEE Service Center, Barcelona, Spain, pp 2068–2075

  • Mininno E, Neri F (2010) A memetic differential evolution approach in noisy optimization. Memet Comput 2(2):111–135

    Article  Google Scholar 

  • Moscato P (1989) On evolution, search, optimization, genetic algorithms and martial arts: towards memetic algorithms. Caltech concurrent computation program, C3P Report, 826:1989

  • Neri F, Cotta C, Moscato P (2012) Handbook of memetic algorithms, vol. 379. Springer, Berlin

  • Neri F, Iacca G, Mininno E (2011) Disturbed exploitation compact differential evolution for limited memory optimization problems. Inf Sci 181(12):2469–2487

    Article  MathSciNet  Google Scholar 

  • Neri F, Tirronen V (2009) Scale factor local search in differential evolution. Memet Comput Springer 1(2):153–171

    Article  Google Scholar 

  • Nguyen QH, Ong YS, Lim MH (2009) A probabilistic memetic framework. IEEE Trans Evol Comput 13(3):604–623

    Article  Google Scholar 

  • Oh S, Hori Y (2006) Development of golden section search driven particle swarm optimization and its application. In SICE-ICASE, 2006. International Joint Conference. IEEE, pp 2868–2873

  • Ong YS, Keane A.J (2004) Meta-lamarckian learning in memetic algorithms. IEEE Trans Evol Comput 8(2):99–110

    Article  Google Scholar 

  • Ong YS, Lim M, Chen X (2010) Memetic computationpast, present and future [research frontier]. Comput Intell Mag IEEE 5(2):24–31

    Article  Google Scholar 

  • Ong YS, Lim MH, Zhu N, Wong KW (2006) Classification of adaptive memetic algorithms: a comparative study. Syst Man Cybernet, Part B: Cybernet, IEEE Trans 36(1):141–152

    Article  Google Scholar 

  • Ong YS, Nair PB, Keane A.J (2003) Evolutionary optimization of computationally expensive problems via surrogate modeling. AIAA J 41(4):687–696

    Article  Google Scholar 

  • Onwubolu GC, Babu BV (2004) New optimization techniques in engineering. Springer, Berlin

  • Passino KM (2002) Biomimicry of bacterial foraging for distributed optimization and control. Control Syst Mag IEEE 22(3):52–67

    Article  MathSciNet  Google Scholar 

  • Price KV, Storn RM, Lampinen JA (2005) Differential evolution: a practical approach to global optimization. Springer, Berlin

  • Ragsdell KM, Phillips DT (1976) Optimal design of a class of welded structures using geometric programming. ASME J Eng Ind 98(3):1021–1025

    Article  Google Scholar 

  • Rao SS, Rao SS (2009) Engineering optimization: theory and practice. Wiley, New York

  • Repoussis PP, Tarantilis CD, Ioannou G (2009) Arc-guided evolutionary algorithm for the vehicle routing problem with time windows. Evol Comput IEEE Trans 13(3):624–647

    Article  Google Scholar 

  • Richer JM, Goëffon A, Hao JK (2009) A memetic algorithm for phylogenetic reconstruction with maximum parsimony. Evoltionary Computation, Machine Learning and Data Mining in Bioinformatics, pp 164–175

  • Ruiz-Torrubiano R, Suárez A (2010) Hybrid approaches and dimensionality reduction for portfolio selection with cardinality constraints. Comput Intell Mag IEEE 5(2):92–107

    Article  Google Scholar 

  • Sandgren E (1990) Nonlinear integer and discrete programming in mechanical design optimization. J Mech Des 112:223

    Google Scholar 

  • Sharma H, Chand Bansal J, Arya KV (2012) Opposition based lTvy flight artificial bee colony. Memet Comput. doi:10.1007/s12293-012-0104-0, December (2012)

  • 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. In CEC 2005

  • Susan J (1999) The meme machine. Oxford University Press, Oxford

  • Tan KC (2005) Eik fun khor, tong heng lee, multiobjective evolutionary algorithms and applications (advanced information and knowledge processing)

  • Tang K, Mei Y, Yao X (2009) Memetic algorithm with extended neighborhood search for capacitated arc routing problems. IEEE Trans Evol Comput 13(5):1151–1166

    Article  Google Scholar 

  • Thakur Deep M.K. (2007) A new crossover operator for real coded genetic algorithms. Appl Math Comput 188(1):895–911

    Article  MathSciNet  MATH  Google Scholar 

  • Valenzuela J, Smith AE (2002) A seeded memetic algorithm for large unit commitment problems. J Heuristics 8(2):173–195

    Article  Google Scholar 

  • Vesterstrom J, Thomsen RA (2004) comparative study of differential evolution, particle swarm optimization, and evolutionary algorithms on numerical benchmark problems. In: Evolutionary Computation, 2004. CEC2004. Congress on, vol. 2. IEEE, pp 1980–1987

  • Wang H, Wang D, Yang S (2009) A memetic algorithm with adaptive hill climbing strategy for dynamic optimization problems. Soft Comput-A Fusion Found Methodol Appl 13(8):763–780

    Article  Google Scholar 

  • Williamson DF, Parker RA, Kendrick JS (1989) The box plot: a simple visual method to interpret data. Ann Intern Med 110(11):916

    Article  Google Scholar 

  • Yang XS (2011) Nature-inspired metaheuristic algorithms. Luniver Press, UK

  • 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

Author information

Authors and Affiliations

  1. ABV-Indian Institute of Information Technology and Management, Gwalior, India

    Jagdish Chand Bansal, Harish Sharma & K. V. Arya

  2. Department of Computer and Mathematical Sciences, Centre for Applicable Mathematics and Systems Science (CAMSS), Liverpool Hope University, Liverpool, L16 9JD, UK

    Atulya Nagar

Authors
  1. Jagdish Chand Bansal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Harish Sharma
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. K. V. Arya
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Atulya Nagar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Harish Sharma.

Additional information

Communicated by G. Acampora.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Bansal, J.C., Sharma, H., Arya, K.V. et al. Memetic search in artificial bee colony algorithm. Soft Comput 17, 1911–1928 (2013). https://doi.org/10.1007/s00500-013-1032-8

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-013-1032-8

Keywords

Search

Navigation