×

A hybrid artificial bee colony optimizer by combining with life-cycle, Powell’s search and crossover. (English) Zbl 1338.90473

Summary: This paper proposes a hybrid artificial bee colony optimizer (HABC) by restructuring the artificial bee colony system with life-cycle, Powell’s search and social learning. The proposed HABC based on life-cycle is a cooperative and varying-population model where the bee can switch its state periodically according to the local environmental landscape. Through this new characteristic, two significant merits of reducing redundant search and maintaining diversity of population can be obtained. In addition, with the social learning, the information exchange ability of the bees can be enhanced in the early exploration phase while the Powell’s method enables the bees to deeply exploit around the promising area, which provides an appropriate balance between exploration and exploitation. Then, eight basic benchmarks, seven CEC 2005 composite functions, and a real-world problem of RFID networks optimization are solved by HABC, successively. The experimental results validate the incorporated combinatorial strategies and demonstrate the performance superiority of HABC.

MSC:

90C59 Approximation methods and heuristics in mathematical programming

Software:

ABC
PDFBibTeX XMLCite
Full Text: DOI

References:

[2] Dorigo, M.; Gambardella, L. M., Ant colony system: a cooperating learning approach to the travelling salesman problem, IEEE. Trans. Evol. Comput., 1, 1, 53-66 (1997)
[3] Passino, K. M., Biomimicry of bacterial foraging for distributed optimization and control, IEEE Control Syst. Mag., 22, 52-67 (2002)
[4] Clerc, M.; Kennedy, J., The particle swarm-explosion, stability, and convergence in a multidimensional complex space, IEEE Trans. Evol. Comput., 6, 1, 58-73 (2002)
[5] Sumathi, S.; Hamsapriya, T.; Surekha, P., Evolutionary Intelligence: An Introduction to Theory and Applications with Matlab (2008), Springer
[6] Hansen, N.; Ostermeier, A., Completely derandomized self-adaptation in evolution strategies, Evol. Comput., 9, 2, 159-195 (2001)
[8] Karaboga, D.; Basturk, B., A powerful and efficient algorithm for numerical function optimization: artificial bee colony (abc) algorithm, J. Glob. Optim., 39, 3, 459-471 (2007) · Zbl 1149.90186
[9] Karaboga, D.; Basturk, B., Artificial bee colony (ABC) optimization algorithm for solving constrained optimization problems, Lect. Notes Comput. Sci., 4529, 789-798 (2007)
[10] Pan, Q. K.; Tasgetiren, M. F.; Suganthan, P. N.; Chua, T. J., A discrete artificial bee colony algorithm for the lot-streaming flow shop scheduling problem, Inf. Sci., 181, 2455-2468 (2011)
[11] Karaboga, D.; Akay, B.; Ozturk, C., Artificial bee colony (ABC) optimization algorithm for training feed-forward neural networks, (Modeling Decisions for Artificial Intelligence (2007), Springer: Springer Berlin Heidelberg), 318-329
[12] Karaboga, D.; Akay, B., A comparative study of artificial bee colony algorithm, Appl. Math. Comput., 214, 108-132 (2009) · Zbl 1169.65053
[13] Biswas, S.; Kundu, S.; Das, S.; Vasilakos, A. V., Information sharing in bee colony for detecting multiple niches in non-stationary environments, (Blum, Christian, Proceeding of the Fifteenth Annual Conference Companion on Genetic and Evolutionary Computation Conference Companion (GECCO 13 Companion), Amsterdam, The Netherlands, July 6-10 (2013), ACM: ACM NY, USA), 1-2
[14] Akbari, R.; Hedayatzadeh, R.; Ziarati, K.; Hassanizadeh, B., A multi-objective artificial bee colony algorithm, Swarm Evol. Comput., 2, 39-52 (2012)
[15] Gao, W.; Liu, S.; Huang, L., Enhancing artificial bee colony algorithm using more information-based search equations, Inf. Sci., 270, 112-133 (2014) · Zbl 1341.68201
[16] Zhu, G. P.; Kwong, S., Gbest-guided artificial bee colony algorithm for numerical function optimization, Appl. Math. Comput., 217, 7, 3166-3173 (2010) · Zbl 1204.65074
[17] Banharnsakun, A.; Achalakul, T.; Sirinaovakul, B., The best-so-far selection in artificial bee colony algorithm, Appl. Soft. Comput., 11, 2, 2888-2901 (2011)
[18] Li, G. Q.; Niu, P. F.; Xiao, X. J., Development and investigation of efficient artificial bee colony algorithm for numerical function optimization, Appl. Soft Comput., 12, 1, 320-332 (2012)
[19] Gao, W.; Liu, S.; Huang, L., A novel artificial bee colony algorithm based on modified search equation and orthogonal learning, IEEE Trans. Cybern., 43, 3, 1011-1024 (2013)
[20] Kang, F.; Li, J. J.; Ma, Z. Y., Rosenbrock artificial bee colony algorithm for accurate global optimization of numerical functions, Inf. Sci., 181, 3508-3531 (2011) · Zbl 1242.65124
[21] Yan, X.; Zhu, Y.; Zou, We; Wang, L., A new approach for data clustering using hybrid artificial bee colony algorithm, Neurocomputing, 97, 241-250 (2012)
[22] Gao, W.; Liu, S.; Huang, L., A novel artificial bee colony algorithm with Powell’s method, Appl. Soft. Comput., 13, 9, 3763-3775 (2013)
[23] Alatas, B., Chaotic bee colony algorithms for global numerical optimization, Expert Syst. Appl., 37, 5682-5687 (2010)
[24] Li, M. S.; Ji, T. Y.; Tang, W. J.; Wu, Q. H.; Saunders, J. R., Bacterial foraging algorithm with varying population, BioSystems, 100, 185-197 (2010)
[25] Niu, B.; Zhu, Y. L.; He, X. X., A lifecycle model for simulating bacterial evolution, Neurocomputing, 72, 1, 142-148 (2008)
[26] Krink, T.; Løvbjerg, M., The lifecycle model: combining particle swarm optimisation, genetic algorithms and hillclimbers, (Parallel Problem Solving from Nature—PPSN VII (2002), Springer: Springer Berlin Heidelberg), 621-630
[27] Powell, M. J.D., Restart procedures for the conjugate gradient method, Math. Program., 12, 241-254 (1977) · Zbl 0396.90072
[28] Ma, L.; Hu, K.; Zhu, Y., Discrete and continuous optimization based on hierarchical artificial bee colony optimizer, J. Appl. Math., 2014 (2014) · Zbl 1406.90131
[29] Liang, J. J.; Qin, A. K.; Suganthan, P. N.; Baskar, S., Comprehensive learning particle swarm optimizer for global optimization of multimodal functions, IEEE. Trans. Evol. Comput., 10, 3, 281-295 (2006)
[30] Salomon, R., Reevaluating genetic algorithm performance under coordinate rotation of benchmark functions. A survey of some theoretical and practical aspects of genetic algorithms, Biosystems, 39, 263-278 (1996)
[31] Yan, X.; Zhu, Y.; Zhang, H., An adaptive bacterial foraging optimization algorithm with lifecycle and social learning, Discrete Dyn. Nat. Soc. (2012), (2012, Article ID 409478, 20 pages) · Zbl 1253.90224
[32] Derrac, J.; García, S.; Molina, D., A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms, Swarm Evol. Comput., 1, 1, 3-18 (2011)
[33] Ma, L.; Zhu, Y. L.; Hu, K. Y.; Chen, H., Cooperative artificial bee colony algorithm for multi-objective RFID network planning, J. Network Comput. Appl., 42, 143-162 (2014)
[34] Chen, H. N.; Zhu, Y. L.; Hu, K. Y., Multi-colony bacteria foraging optimization with cell-to-cell communication for RFID network planning, Appl. Soft Comput., 10, 2, 539-547 (2010)
[35] Liao, I.; Kao, K., Enhancing the accuracy of WLAN-based location determination systems using predicted orientation information, Inf. Sci., 178, 4, 1049-1068 (2008)
[37] Dobkin, D. M., The RF in RFID: Passive UHF RFID in Practice (2004), Elsevier, pp. 68-77
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.