Abstract
The artificial bee colony (ABC) algorithm is one of the popular swarm intelligence algorithms that is inspired by the forging behavior of honeybee colonies. To improve the convergence precision of the ABC algorithm, accelerate the search speed of finding the best solution and control the balance between exploration and exploitation, we propose an improved double-population ABC algorithm based on heterogeneous comprehensive learning (HCLIABC). In this algorithm, the swarm is divided into exploration-subpopulation named group 1 and exploitation-subpopulation named group 2. Illuminated by particle swarm optimization (PSO), the food source will be updated on all dimensions rather than on a randomly selected dimension. Meanwhile HCL strategy is used to generate the exemplars for two subpopulations. In addition, opposition-based learning is used to improve the quality of initial swarm, and multiplicative weight update method is used to update the selection probability of the double-population in employed bees phase. To evaluate the remarkable performance of the improved algorithm, we conduct comparative experiments of 18 unimodal, multimodal, and rotated benchmark functions on dimensions 30 and 100. Computational results demonstrate that HCLIABC can effectively prevent premature convergence and produce competitive optimization precision and convergence speed compared with several popular and classic DE, PSO and ABC variants.
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Arora S, Hazan E, Kale S (2012) The multiplicative weights update method: a meta algorithm and applications. Theory Comput 8(6):121–164
Atkinson Anthony C, Riani M (1997) Bivariate boxplots, multiple outliers, multivariate transformations and discriminant analysis: the 1997 Hunter lecture. Environmetrics 8(6):583–602
Brest J, Greiner S, Boskovic B, Mernik M, Zumer V (2006) Self-adapting control parameters in differential evolution: a comparative study on numerical bench-mark problems. IEEE Trans Evol Comput 10(6):646–657
Basturk B, Karaboga D (2006) An artificial bee colony (ABC) algorithm for numeric function optimization. In: IEEE swarm intelligence symposium, vol 8, no 1, pp 687–697
Baykasoglu A, Ozbakir L, Tapkan P (2007) Artificial bee colony algorithm and its application to generalized assignment problem. In: Swarm intelligence: focus on ant and particle swarm optimization, vol 8. Itech Education and Publishing, Vienna, pp 113–144
Chen H, Zhu Y, Hu K, He X (2010) Hierarchical swarm model: a new approach to optimization. Discrete Dyn Nat Soc 2010:1038–1045
Dorigo M, Gambardella LM (1997) Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans Evol Comput 1(1):53–66
Dragoi EN, Dafinescu V (2016) Parameter control and hybridization techniques in differential evolution: a survey. Artif Intell Rev 45(4):1–24
Eberhart R (1998) A modified particle swarm optimizer. In: Proceedings of the 1998 IEEE world congress on computational intelligence, vol 6. IEEE Press, Piscataway, NJ, pp 69–73
Forrest S (1993) Genetic algorithms: principles of natural selection applied to computation. Science 261(5123):872–878
Goldberg DE, Holland JH (1988) Genetic algorithms and machine learning. Mach Learn 3(2–3):95–99
Gong W, Cai Z, Ling CX (2010) DE/BBO: a hybrid differential evolution with biogeography-based optimization for global numerical optimization. Soft Comput 15(4):645–665
Hintze JL, Nelson RD (1998) Violin plots: a box plot-density trace synergism. Am Stat 52(2):181–184
Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. Technical report Engineering Faculty, Computer Engineering Department. Erciyes University
Karaboga D, Basturk B (2007a) Artificial bee colony (ABC) optimization algorithm for solving constrained optimization problems. In: International fuzzy systems association world congress, vol 11. IFSA, Mexico, pp 789–798
Karaboga D, Basturk B (2007b) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Glob Optim 39(3):459–471
Karaboga D, Basturk B (2008) On the performance of artificial bee colony (ABC) algorithm. Appl Soft Comput 8(1):687–697
Karaboga D, Akay B, Ozturk C (2007) Artificial bee colony (ABC) optimization algorithm for training feed-forward neural networks. In: International conference on modeling decisions for artificial intelligence, vol 4617. Springer, Berlin, pp 318–329
Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceeding of the IEEE international conference on neural network, vol 4. Perth, Australia, pp 1942–1948
Li X (2012) Cooperatively coevolving particle swarms for large scale optimization. IEEE Trans Evol Comput 16(2):210–224
Liang JJ, Qin AK, Suganthan PN (2006) Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans Evol Comput 10(3):281–295
Lynn N, Suganthan PN (2015) Heterogeneous comprehensive learning particle swarm optimization with enhanced exploration and exploitation. Swarm Evol Comput 24:11–24
Ma L, Hu K, Zhu Y (2014) Discrete and continuous optimization based on hierarchical artificial bee colony optimizer. J Appl Math 2014:1–20
Merchant A (2014) Multiplicative weights update: a useful addition to an algorithmist’s toolkit. http://researchweb.iiit.ac.in/arpit.merchant/public-ations/report.pdf
McGill R, Tukey JW, Larsen WA (1978) Variations of boxplots. Am Stat 1:12–16
Nelson LS (1989) Evaluating overlapping confidence intervals. J Qual Technol 21:140–141
Niu B, Zhu Y, He X, Wu H (2007) MCPSO: a multi-swarm cooperative particle swarm optimizer. Appl Math Comput 185(2):1050–1062
Passino KM (2002) Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Syst Mag 22(3):52–67
Qiu X, Xu JX, Tan KC (2016) Adaptive cross-generation differential evolution operators for multiobjective optimization. IEEE Trans Evol Comput 20(2):232–244
Rahnamayan S, Wang GG (2008) Solving large scale optimization problems by opposition based differential evolution (ODE). WSEAS Trans Comput 7(10):1792–1804
Rahnamayan S, Tizhoosh HR, Salama MMA (2007) A novel population initialization method for accelerating evolutionary algorithms. Comput Math Appl 53(10):1605–1614
Rahnamayan S, Tizhoosh HR, Salama MMA (2008) Opposition-based differential evolution. IEEE Trans Evol Comput 12(1):64–79
Tasgetiren MF, Pan Q, Suganthan PN, Chen AH (2011) A discrete artificial bee colony algorithm for the total flowtime minimization in permutation flow shops. Inf Sci 181(16):3459–3475
Tizhoosh HR (2006) Opposition-based reinforcement learning. J Adv Comput Intell Inform 10(4):578–585
Van den Bergh F, Engelbrecht AP (2004) A cooperative approach to particle swarm optimization. IEEE Trans Evol Comput 8(3):225–239
Wu G (2016) Across neighborhood search for numerical optimization. Inf Sci 329:597–618
Wu G, Mallipeddi R, Suganthan PN (2016) Differential evolution with multi-population based ensemble of mutation strategies. Inf Sci 329:329–345
Xiang Y, Peng Y, Zhong Y (2014) A particle swarm inspired multi-elitist artificial bee colony algorithm for real-parameter optimization. Comput Optim Appl 57(2):493–516
Yang X, Huang Z (2012) Opposition-based artificial bee colony with dynamic cauchy mutation for function optimization. Int J Adv Comput Technol 4(4):56–62
Zhang J, Sanderson AC (2009) JADE: adaptive differential evolution with optional external archive. IEEE Trans Evol Comput 13(5):945–958
Acknowledgements
This work was supported by the National Natural Science Foundation of China (Grant Nos. 11431002, 11371197), a project funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) and the Foundation of Jiangsu Key Lab for NSLSCS (Grant No. 201601). The authors thank the anonymous reviewers for providing valuable comments to improve this paper and add special thanks to Professor Suganthan PN for providing the source codes of the comparative algorithms (HCLPSO). The authors also thank Professor Wu Guohua for providing the excellent simulation results of jDE and JADE on 30D and 100D of the 18 benchmark functions.
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No conflict of interest exits in the submission of this article, and it is approved by all authors for publication. I would like to declare on behalf of my co-authors that the work was original research that has not been published previously, and not under consideration for publication elsewhere, in whole or in part.
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Wang, J., Sun, Y. & Liu, F. An improved double-population artificial bee colony algorithm based on heterogeneous comprehensive learning. Soft Comput 22, 6489–6514 (2018). https://doi.org/10.1007/s00500-017-2700-x
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DOI: https://doi.org/10.1007/s00500-017-2700-x