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.

Keywords


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

Full Text:

PDF

References


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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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




DOI: https://doi.org/10.15837/ijccc.2018.4.3275



Copyright (c) 2018 Xuemei You

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

CC-BY-NC  License for Website User

Articles published in IJCCC user license are protected by copyright.

Users can access, download, copy, translate the IJCCC articles for non-commercial purposes provided that users, but cannot redistribute, display or adapt:

  • Cite the article using an appropriate bibliographic citation: author(s), article title, journal, volume, issue, page numbers, year of publication, DOI, and the link to the definitive published version on IJCCC website;
  • Maintain the integrity of the IJCCC article;
  • Retain the copyright notices and links to these terms and conditions so it is clear to other users what can and what cannot be done with the  article;
  • Ensure that, for any content in the IJCCC article that is identified as belonging to a third party, any re-use complies with the copyright policies of that third party;
  • Any translations must prominently display the statement: "This is an unofficial translation of an article that appeared in IJCCC. Agora University  has not endorsed this translation."

This is a non commercial license where the use of published articles for commercial purposes is forbiden. 

Commercial purposes include: 

  • Copying or downloading IJCCC articles, or linking to such postings, for further redistribution, sale or licensing, for a fee;
  • Copying, downloading or posting by a site or service that incorporates advertising with such content;
  • The inclusion or incorporation of article content in other works or services (other than normal quotations with an appropriate citation) that is then available for sale or licensing, for a fee;
  • Use of IJCCC articles or article content (other than normal quotations with appropriate citation) by for-profit organizations for promotional purposes, whether for a fee or otherwise;
  • Use for the purposes of monetary reward by means of sale, resale, license, loan, transfer or other form of commercial exploitation;

    The licensor cannot revoke these freedoms as long as you follow the license terms.

[End of CC-BY-NC  License for Website User]


INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL (IJCCC), With Emphasis on the Integration of Three Technologies (C & C & C),  ISSN 1841-9836.

IJCCC was founded in 2006,  at Agora University, by  Ioan DZITAC (Editor-in-Chief),  Florin Gheorghe FILIP (Editor-in-Chief), and  Misu-Jan MANOLESCU (Managing Editor).

Ethics: This journal is a member of, and subscribes to the principles of, the Committee on Publication Ethics (COPE).

Ioan  DZITAC (Editor-in-Chief) at COPE European Seminar, Bruxelles, 2015:

IJCCC is covered/indexed/abstracted in Science Citation Index Expanded (since vol.1(S),  2006); JCR2016: IF=1.374. .

IJCCC is indexed in Scopus from 2008 (CiteScore 2017 = 1.04; SNIP2017 = 0.616, SJR2017 =0.326):

Nomination by Elsevier for Journal Excellence Award Romania 2015 (SNIP2014 = 1.029): Elsevier/ Scopus

IJCCC was nominated by Elsevier for Journal Excellence Award - "Scopus Awards Romania 2015" (SNIP2014 = 1.029).

IJCCC is in Top 3 of 157 Romanian journals indexed by Scopus (in all fields) and No.1 in Computer Science field by Elsevier/ Scopus.

 

 Impact Factor in JCR2017 (Clarivate Analytics/SCI Expanded/ISI Web of Science): IF=1.29 (Q3). Scopus: CiteScore2017=1.04 (Q2); Editors-in-Chief: Ioan DZITAC & Florin Gheorghe FILIP.