An ABC Algorithm with Recombination

  • Xuemei You
  • Yinghong Ma
  • Zhiyuan Liu
  • Mingzhao Xie

Abstract

Artificial bee colony (ABC) is an efficient swarm intelligence algorithm, which has shown good exploration ability. However, its exploitation capacity needs to be improved. In this paper, a novel ABC variant with recombination (called RABC) is proposed to enhance the exploitation. RABC firstly employs a new search model inspired by the updating equation of particle swarm optimization (PSO). Then, both the new search model and the original ABC model are recombined to build a hybrid search model. The effectiveness of the proposed RABC is validated on ten famous benchmark optimization problems. Experimental results show RABC can significantly improve the quality of solutions and accelerate the convergence speed.

References

[1] Akay, B.; Karaboga, D. (2012); A modified Artificial bee colony algorithm for real-parameter optimization, Information Sciences, 192, 120-142, 2012.
https://doi.org/10.1016/j.ins.2010.07.015

[2] Cai, X.; Wang, H.; Cui, Z.; Cai, J.; Xue, Y.; Wang, L.(2018); Bat algorithm with triangleflipping strategy for numerical optimization, International Journal of Machine Learning and Cybernetics, 9(2), 199-215, 2018.
https://doi.org/10.1007/s13042-017-0739-8

[3] Chen, X.; Xu, B.; Mei, C.; Ding, Y.; Li, K. (2018); Teaching Clearning Cbased artificial bee colony for solar photovoltaic parameter estimation, Applied Energy, 212, 1578-1588, 2018.
https://doi.org/10.1016/j.apenergy.2017.12.115

[4] Cui, L.; Li, G.; Wang, Z.; Lin, Q.; Chen, J.; Lu, N.; Lu, J. (2017); A ranking-based adaptive artificial bee colony algorithm for global numerical optimization, Information Sciences, 417, 169-185, 2017.
https://doi.org/10.1016/j.ins.2017.07.011

[5] Cui, Z.H.; Sun, B.; Wang, G.G.; Xue, Y.; Chen, J.J. (2017); A novel oriented cuckoo search algorithm to improve DV-Hop performance for cyber-physical systems, Journal of Parallel and Distributed Computing, 103, 42-52, 2017.
https://doi.org/10.1016/j.jpdc.2016.10.011

[6] Cui, L.; Li, G.; Zhu, Z.; Lin, Q.; Chen, J. (2017); A novel artificial bee colony algorithm with an adaptive population size for numerical function optimization, Information Sciences, 414, 53-67, 2017.
https://doi.org/10.1016/j.ins.2017.05.044

[7] Gao, W.; Liu, S. (2012); A modified artificial bee colony algorithm, Computers & Operations Research, 39, 687-697, 2012.
https://doi.org/10.1016/j.cor.2011.06.007

[8] Huang, P.; Lin, F.; Xu, L.J.; Kang, Z.L.; Zhou, J.L.; Yu, J.S. (2017); Improved ACObsed seep coverage scheme considering data delivery, International Journal of Simulation Modelling, 16(2), 289-301, 2017.
https://doi.org/10.2507/IJSIMM16(2)9.385

[9] Karaboga, D. (2005); An idea based on honey bee swarm for numerical optimization, Technical Report-TR06, Erciyes University, engineering Faculty, Computer Engineering Department, 2005.

[10] Karaboga, D.; Akay, B. (2009); A comparative study of artificial bee colony algorithm, Applied Mathematics and Computation, 214, 108-132, 2009.
https://doi.org/10.1016/j.amc.2009.03.090

[11] Kennedy, J.; Eberhart, R. (1995); Particle swarm optimization, Proceedings of IEEE International Conference on Neural Networks, 1942-1948, 1995.

[12] Kong, D.; Chang, T.; Dai, W.; Wang, Q.; Sun, H. (2018); An improved artificial bee colony algorithm based on elite group guidance and combined breadth-depth search strategy, Information Sciences, 442-443, 54-71, 2018.
https://doi.org/10.1016/j.ins.2018.02.025

[13] Li, G.; Cui, L.; Fu, X.; Wen, Z.; Lua, N.; Lu, J. (2017); Artificial bee colony algorithm with gene recombination for numerical function optimization, Applied Soft Computing, 52, 146-159, 2017.
https://doi.org/10.1016/j.asoc.2016.12.017

[14] Li, J.; Pan, Q.; Xie, S.; Wang, S. (2011); A Hybrid Artificial Bee Colony Algorithm for Flexible Job Shop Scheduling Problems, International Jotrnal of Computers Communications & Control, 6(2), 286-296, 2011.
https://doi.org/10.15837/ijccc.2011.2.2177

[15] Li, G.; Niu, P.; Xiao, X. (2012); Development and investigation of efficient artificial bee colony algorithm for numerical function optimization, Applied Soft Computing, 12(1), 320- 332, 2012.
https://doi.org/10.1016/j.asoc.2011.08.040

[16] Liu, J.J.; Zhu, H.Q.; Ma, Q.; Zhang, L.L.; Xu, H.L. (2015); An artificial bee colony algorithm with guide of global & local optima and asynchronous scaling factors for numerical optimization, Soft Computing, 37, 608-618, 2015.
https://doi.org/10.1016/j.asoc.2015.08.021

[17] Rajput, U.; Kumari, M. (2017); Mobile robot path planning with modified ant colony optimisation, International Journal of Bio-Inspired Computation, 9(2), 106-113, 2017.
https://doi.org/10.1504/IJBIC.2017.083133

[18] Song, X.; Yan, Q.; Zhao, M. (2017); An adaptive artificial bee colony algorithm based on objective function value information, Applied Soft Computing, 55, 384-401, 2017.
https://doi.org/10.1016/j.asoc.2017.01.031

[19] Sulaiman, N.; Mohamad-Saleh, J.; Abro, A.G. (2017); Robust variant of artificial bee colony (JA-ABC4b) algorithm, International Journal of Bio-Inspired Computation, 10(2), 99-108, 2017.
https://doi.org/10.1504/IJBIC.2017.085896

[20] Wang, H.;Wang, W.; H. Sun, H.; Rahnamayan, S. (2016); Firefly algorithm with random attraction, International Journal of Bio-Inspired Computation, 8(1), 33-41, 2016.
https://doi.org/10.1504/IJBIC.2016.074630

[21] Wang, H.; Rahnamayan, S.; Sun, H.; Omran, M.G.H. (2013); Gaussian bare-bones differential evolution, IEEE Transactions on Cybernetics, 43(2), 634-647, 2013.
https://doi.org/10.1109/TSMCB.2012.2213808

[22] Wang, H.; Wu, Z.; Rahnamayan, S.; Liu, Y.; Ventresca, M. (2011); Enhancing particle swarm optimization using generalized opposition-based learning, Information Sciences, 181(20), 4699-4714, 2011.
https://doi.org/10.1016/j.ins.2011.03.016

[23] Wang, H.; Wu, Z.J.;Rahnamayan, S.; Sun, H.; Liu, Y.; Pan, J.S. (2014); Multi-strategy ensemble artificial bee colony algorithm, Information Sciences, 279, 587-603, 2014.
https://doi.org/10.1016/j.ins.2014.04.013

[24] H. Wang; H. Sun; C, Li; S. Rahnamayan; J.S. Pan; Diversity enhanced particle swarm optimization with neighborhood search, Information Sciences, 223, 119-135, 2013.
https://doi.org/10.1016/j.ins.2012.10.012

[25] Wu, J.; Wu, G.D.; Wang, J.J. (2017); Flexible job-shop scheduling problem based on hybrid ACO algorithm, International Journal of Simulation Modelling, 16(3), 497-505, 2017.
https://doi.org/10.2507/IJSIMM16(3)CO11

[26] Xiang, W.; Li, Y.; Meng, X.; Zhang, C.; An, M. (2017); A grey artificial bee colony algorithm, Applied Soft Computing, 60, 1-17, 2017.
https://doi.org/10.1016/j.asoc.2017.06.015

[27] Xiang, W.; Li, Y.; He, R.; Gao, M.; An, M. (2018); A novel artificial bee colony algorithm based on the cosine similarity, Computers & Industrial Engineering, 115, 54-68, 2018.
https://doi.org/10.1016/j.cie.2017.10.022

[28] Xiang, Y.; Peng, Y.M.; Zhong, Y.B.; Chen, Z.Y.; Lu, X.W.; Zhong, X.J. (2014); A particle swarm inspired multi-elite artificial bee colony algorithm for real-parameter optimization, Computational Optimization and Applications, 57, 493-516, 2014.
https://doi.org/10.1007/s10589-013-9591-2

[29] Yaghoobi, T.; Esmaeili, E. (2017); An improved artificial bee colony algorithm for global numerical optimisation, International Journal of Bio-Inspired Computation, 9(4), 251-258, 2017.
https://doi.org/10.1504/IJBIC.2017.084318

[30] Zhang, M.; Wang, H.; Cui, Z.; Chen, J. (2017); Hybrid Multi-objective cuckoo search with dynamical local search, Memetic Computing, doi: 10.1007/s12293-017-0237-2, 2017.
https://doi.org/10.1007/s12293-017-0237-2

[31] Zhou, X.; Wu, Z.; Wang, H.; Rahnamayan, S. (2016); Gaussian bare-bones artificial bee colony algorithm[J], Soft Computing, 20(3), 907-924, 2016.
https://doi.org/10.1007/s00500-014-1549-5

[32] Zhu, G.; Kwong, S. (2010); Gbest-guided artificial bee colony algorithm for numerical function optimization, Applied Mathematics and Computation, 217, 3166-3173, 2010.
https://doi.org/10.1016/j.amc.2010.08.049
Published
2018-07-25
How to Cite
YOU, Xuemei et al. An ABC Algorithm with Recombination. INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL, [S.l.], v. 13, n. 4, p. 590-601, july 2018. ISSN 1841-9844. Available at: <http://univagora.ro/jour/index.php/ijccc/article/view/3275>. Date accessed: 05 july 2020. doi: https://doi.org/10.15837/ijccc.2018.4.3275.

Keywords

Artificial bee colony (ABC), recombination, hybrid search model, global optimization