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Design of Recursive Digital Filters with Penalized Spline Method Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Diversifying Search in Bee Algorithms for Numerical Optimisation

Authors : Muharrem Düg̃enci, Mehmet Emin Aydin

Published in: Computational Collective Intelligence

Publisher: Springer International Publishing

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Abstract

Swarm intelligence offers useful instruments for developing collective behaviours to solve complex, ill-structured and large-scale problems. Efficiency in collective behaviours depends on how to harmonise the individual contributions so that a complementary collective effort can be achieved to offer a useful solution. The harmonisation helps blend diversification and intensification suitably towards efficient collective behaviours. In this study, two renown honeybees-inspired algorithms were analysed with respect to the balance of diversification and intensification and a hybrid algorithm is proposed to improve the efficiency accordingly. The proposed hybrid algorithm was tested with solving well-known highly dimensional numerical optimisation (benchmark) problems. Consequently, the proposed hybrid algorithm has demonstrated outperforming the two original bee algorithms in solving hard numerical optimisation benchmarks.

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Literature
1.
2.
Alam, M.S., Islam, M.M., Yao, X.: Recurring two-stage evolutionary programming: a novel approach for numerical optimizaiton. IEEE Trans. Syst. Man. Cybern. Part B: Cybern. 41(5), 1352–1365 (2011)CrossRef
3.
Aydin, M.E.: Coordinating metaheuristic agents with swarm intelligence. J. Intell. Manufact. (Springer) 23(4), 991–999 (2012)CrossRef
4.
Aydog̃du, I., Akin, A., Saka, M.P.: Design optimization of real world steel space frames using artificial bee colony algorithm with Levy flight distribution. Adv. Eng. Softw. 92, 1–14 (2016)CrossRef
5.
Cui, L., Li, G., Zhu, Z., Lin, Q., Wen, Z., Lu, N., Chen, J.: A novel artificial bee colony algorithm with an adaptive population size for numerical function optimization. Inf. Sci. 414, 53–67 (2017)MathSciNetCrossRef
6.
Dogan, B., Olmez, T.: A new metaheuristics for numerical function optimization: Vortex Search algorithm. Inf. Sci. 293, 125–145 (2015)CrossRef
7.
8.
Gong, W., Cai, Z., Jia, L., Li, H.: A generalized hybrid generation scheme of differential evolution for global numerical optimization. Int. J. Comput. Intell. Appl. 10, 35–65 (2011)CrossRef
9.
Guo, L., Wang, G.-G., Gandomi, A.H., Alavi, A.H., Duan, H.: A new improved krill herd algorithm for global numerical optimization. Neurocomputing 138, 392–402 (2014)CrossRef
10.
Hacıbeyoğlu, M., Koçer, B., Arslan, A.: Transfer learning for artificial bee colony algorithm to optimize numerical functions. In: International Conference on Computer Engineering and Network Security (ICCENS 2012), Dubai (2012)
11.
Han, M., Liu, C., Xing, J.: An evolutionary membrane algorithm for global optimization problems. Inf. Sci. 276, 219–241 (2014)MathSciNetCrossRef
12.
Hussein, W.A., Sahran, S., Abdullah, S.N.H.S.: Patch-Levy-based initialization algorithm for Bees algorithm. Appl. Soft Comput. 23, 104–121 (2014)CrossRef
13.
Karaboga, D.: An idea based on honey bee swarm for numerical optimisation. Technical report, Computer Engineering Department, Erciyes University, Kayseri, Turkey (2005)
14.
Karaboga, D., Akay, B.: A comparative study of artificial bee colony algorithm. Appl. Math. Comput. 214, 108–132 (2009)MathSciNetMATH
15.
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)MathSciNetCrossRef
16.
Karaboga, D., Gorkemli, B., Ozturk, C., Karaboga, N.: A comprehensive survey: artificial bee colony (ABC) algorithm and applications. Artif. Intell. Rev. 42(1), 21–57 (2014)CrossRef
17.
Kashan, A.H.: A new metaheuristic for optimization: optics inspired optimization (OIO). Comput. Oper. Res. 55, 99–125 (2015)MathSciNetCrossRef
18.
Keskin, T.E., Düğenci, M., Kaçaroğlu, F.: Prediction of water pollution using artificial neural networks in the study areas of Sivas, Karabük and Bartin (Turkey). Environ. Earth Sci. 73(9), 5333–5347 (2014)CrossRef
19.
Kiran, M.S., Gunduz, M.: A novel artificial bee colony-based algorithm for solving the numerical optimization problems. Int. J. Innov. Comput. Inf. Control 8(9), 6107–6121 (2012)
20.
Kiran, M.S., Findik, O.: A directed artificial bee algorithm. Appl. Soft Comput. 26, 454–462 (2015)CrossRef
21.
Kong, X., Liu, S., Wang, Z., Yong, L.: Hybrid Artificial Bee Colony Algorith for Global Numerical Optimization. Journal of Computational Information Systems 8(6), 2367–2374 (2012)
22.
Liu, Y., Niu, B., Luo, Y.: Hybrid learning particle swarm optimizer with genetic disturbance. Neurocomuting 151, 1237–1247 (2015)CrossRef
23.
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(12), 2455–2468 (2011)MathSciNetCrossRef
24.
Pham, D.T., Ghanberzadeh, A., Koc, E., Otri, S., Rahim, S., Zaidi, M.: The bees algorithm - anovel tool for complex optimisation. In: Intelligent Production Machines and Systems (2006)
25.
Piotrowski, A.P.: Regardin the rankings of optimization heuristics based on artificially constructed functions. Inf. Sci. 297, 191–201 (2015)CrossRef
26.
Rahmani, R., Yusof, R.: A new simple, fast and efficient algorithm for global optimization over continuous search-space problems: radial movement optimization. Appl. Math. Comput. 248, 287–300 (2014)MathSciNetMATH
27.
Suganthan, P.N., et al.: Problem definitions and evaluation criteria for CEC 2005 special session on real-parameter optimization. Technical report, Computer Science, Nanyang Technological University, Singapore, KanGAL, IIT, Kanpur (2005)
28.
Xin, B., Chen, J., Peng, Z.H., Pan, F.: An adaptive hybrid optimizer based on particle swarm and differential evolution for global optimization. Inf. Sci. (Sci. China) 53(5), 980–989 (2010)MathSciNetCrossRef
29.
Yuce, B., Pham, D.T., Packianather, M.S., Mastrocinque, E.: An enhancement to the Bees algorithm with slope angle computation and Hill Climbing algorithm and its applications on scheduling and continuous-type optimisation problem. Prod. Manufact. Res. 3(1), 3–19 (2015)CrossRef
30.
Yuce, B., Packianather, M.S., Mastrocinque, E., Pham, D.T., Lambiase, A.: Honey bees inspired optimization method: the bees algorithm. Insects 4(4), 646–662 (2013)CrossRef
31.
Zhao, R., Tang, W.: Monkey algorithm for global numerical optimization. J. Uncertain Syst. 2(3), 165–176 (2008)
Metadata
Title
Diversifying Search in Bee Algorithms for Numerical Optimisation
Authors
Muharrem Düg̃enci
Mehmet Emin Aydin
Copyright Year
2018
Book
Computational Collective Intelligence

Print ISBN: 978-3-319-98445-2

Electronic ISBN: 978-3-319-98446-9

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-98446-9_13

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