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International Journal of Machine Learning and Cybernetics
Erschienen in: International Journal of Machine Learning and Cybernetics 3/2017

05.04.2015 | Original Article

An improved artificial bee colony algorithm for solving constrained optimization problems

verfasst von: Yaosheng Liang, Zhongping Wan, Debin Fang

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 3/2017

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Abstract

The artificial bee colony (ABC) algorithm is a global stochastic optimization algorithm inspired by simulating the foraging behavior of honey bees. It has been successfully applied to solve the constrained optimization problems (COPs) with a constraint handling technique (Deb’s rules). However, it may also lead to premature convergence. In order to improve this problem, we propose an improved artificial bee colony (I-ABC) algorithm for COPs. In I-ABC algorithm, we firstly relax the Deb’s rules by introducing the approximate feasible solutions to suitably utilize the information of the infeasible solutions with better objective function value and small violation. Next, we construct a selection strategy based on rank selection and design a search mechanism using the information of the best-so-far solution to balance the exploration and the exploitation at different stages. In addition, periodic boundary handling mode is used to repair invalid solutions. To verify the performance of I-ABC algorithm, 24 benchmark problems are employed and two comparison experiments have been carried out. The numerical results show that the proposed I-ABC algorithm has an outstanding performance for the COPs.
Vorheriger Artikel Sequence clustering algorithm based on weighted vector identification
Nächster Artikel A multi-attribute decision-making method with prioritization relationship and dual hesitant fuzzy decision information

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Literatur
1.
Amador-Angulo L, Castillo O, Pulido M (2013) Comparison of fuzzy controllers for the water tank with type-1 and type-2 fuzzy logic. In: IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS), 2013 Joint, IEEE, pp 1062–1067
2.
Bao L, Zeng JC (2009) Comparison and analysis of the selection mechanism in the artificial bee colony algorithm. In: Proceedings of IEEE international conference on hybrid intelligent systems, Shenyang, pp 411–416
3.
Brajevic I, Tuba M (2013) An upgraded artificial bee colony (abc) algorithm for constrained optimization problems. J Intell Manuf 24(4):729–740CrossRef
4.
Brajevic I, Tuba M, Subotic M (2011) Performance of the improved artificial bee colony algorithm on standard engineering constrained problems. Int J Math Comput Simul 5(2):135–143
5.
Cai J, Zhu W, Ding H, Min F (2014) An improved artificial bee colony algorithm for minimal time cost reduction. Int J Mach Learn Cybern 5(5):743–752CrossRef
6.
Cai Z, Wang Y (2006) A multiobjective optimization-based evolutionary algorithm for constrained optimization. IEEE Trans Evol Comput 10(6):658–675CrossRef
7.
Chang WL, Zeng D, Chen RC, Guo S (2013) An artificial bee colony algorithm for data collection path planning in sparse wireless sensor networks. Int J Mach Learn Cybern. doi:10.​1007/​s13042-013-0195-z
8.
Chau K (2007) Application of a pso-based neural network in analysis of outcomes of construction claims. Automat Constr 16(5):642–646CrossRef
9.
Cheng CT, Lin JY, Sun YG, Chau K (2005) Long-term prediction of discharges in manwan hydropower using adaptive-network-based fuzzy inference systems models. In: Advances in natural computation, Springer, pp 1152–1161
10.
Deb K (2000) An efficient constraint handling method for genetic algorithms. Comput Method Appl M 186(2):311–338CrossRefMATH
11.
Hu X, Eberhart R (2002) Solving constrained nonlinear optimization problems with particle swarm optimization. In: Proceedings of the sixth world multiconference on systemics, cybernetics and informatics, Orlando, pp 203–206
12.
Hu Y, Cheung YM, Wang Y (2007) A ranking-based evolutionary algorithm for constrained optimization problems. In: Proceedings of IEEE international conference on natural computation, pp 198–202
13.
Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. Techn Rep TR06, Erciyes Univ Press, Erciyes
14.
Karaboga D, Akay B (2009) A comparative study of artificial bee colony algorithm. Appl Math Comput 214(1):108–132MathSciNetMATH
15.
Karaboga D, Akay B (2011) A modified artificial bee colony (abc) algorithm for constrained optimization problems. Appl Soft Comput 11(3):3021–3031CrossRef
16.
Karaboga D, Basturk B (2007) Artificial bee colony (abc) optimization algorithm for solving constrained optimization problems. In: Foundations of fuzzy logic and soft computing, Springer, pp 789–798
17.
Karaboga D, Gorkemli B, Ozturk C, Karaboga N (2014) A comprehensive survey: artificial bee colony (abc) algorithm and applications. Artif Intell Rev 42(1):21–57CrossRef
18.
Li X, Yin M (2014) Self-adaptive constrained artificial bee colony for constrained numerical optimization. Neural Comput Appl 24(3–4):723–734CrossRef
19.
Liang J, Runarsson TP, Mezura-Montes E, Clerc M, Suganthan P, Coello CC, Deb K (2006) Problem definitions and evaluation criteria for the cec 2006 special session on constrained real-parameter optimization. J Appl Mech 41:1–8
20.
Mezura-Montes E, Cetina-Domínguez O (2009) Exploring promising regions of the search space with the scout bee in the artificial bee colony for constrained optimization. In: Intelligent engineering systems through artificial neural networks, ASME Press, pp 253–260
21.
Mezura-Montes E, Cetina-Domínguez O (2012) Empirical analysis of a modified artificial bee colony for constrained numerical optimization. Appl Math Comput 218(22):10943–10973MathSciNetMATH
22.
Mezura-Montes E, Coello CAC (2005) A simple multimembered evolution strategy to solve constrained optimization problems. IEEE Trans Evol Comput 9(1):1–17CrossRefMATH
23.
Mezura-Montes E, Coello Coello CA (2011) Constraint-handling in nature-inspired numerical optimization: past, present and future. Swarm Evol Comput 1(4):173–194CrossRef
24.
Mezura-Montes E, Velez-Koeppel RE (2010) Elitist artificial bee colony for constrained real-parameter optimization. In: Proceedings of IEEE international conference on evolutionary computation, Barcelona, pp 1–8
25.
Muñoz Zavala AE, Aguirre AH, Villa Diharce ER (2005) Constrained optimization via particle evolutionary swarm optimization algorithm (peso). In: Proceedings of the 2005 conference on genetic and evolutionary computation, USA, pp 209–216
26.
Olivas EL, Castillo O, Valdez F, Soria J (2013) Ant colony optimization for membership function design for a water tank fuzzy logic controller. In: Hybrid intelligent models and applications (HIMA), IEEE, pp 27–34
27.
Parsopoulos KE, Vrahatis MN (2002) Particle swarm optimization method for constrained optimization problems. In: Intelligent technologies-theory and application: new trends in intelligent technologies 76:214–220
28.
Rana S, Jasola S, Kumar R (2013) A boundary restricted adaptive particle swarm optimization for data clustering. Int J Mach Learn Cybern 4(4):391–400CrossRef
29.
Srivastava S, Deb K (2010) A genetic algorithm based augmented lagrangian method for computationally fast constrained optimization. In: Swarm, evolutionary, and memetic computing, Springer, pp 330–337
30.
Sun W, Yuan YX (2006) Optimization theory and methods: nonlinear programming. Springer, New YorkMATH
31.
Takahama T, Sakai S, Iwane N (2005) Constrained optimization by the \(\varepsilon\) constrained hybrid algorithm of particle swarm optimization and genetic algorithm. In: Advances in Artificial Intelligence, Springer, pp 389–400
32.
Tsai HC (2014) Integrating the artificial bee colony and bees algorithm to face constrained optimization problems. Inf Sci 258:80–93MathSciNetCrossRef
33.
Valdez F, Melin P, Castillo O (2014) A survey on nature-inspired optimization algorithms with fuzzy logic for dynamic parameter adaptation. Expert Syst Appl 41(14):6459–6466CrossRef
34.
Zhang J, Chau KW (2009) Multilayer ensemble pruning via novel multi-sub-swarm particle swarm optimization. J UCS 15(4):840–858
35.
Zhang WJ, Xie XF, Bi DC (2004) Handling boundary constraints for numerical optimization by particle swarm flying in periodic search space. In: Proceedings of IEEE international conference on evolutionary computation, Oregon, pp 2307–2311
36.
Zhu G, Kwong S (2010) Gbest-guided artificial bee colony algorithm for numerical function optimization. Appl Math Comput 217(7):3166–3173MathSciNetMATH
Metadaten
Titel
An improved artificial bee colony algorithm for solving constrained optimization problems
verfasst von
Yaosheng Liang
Zhongping Wan
Debin Fang
Publikationsdatum
05.04.2015
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 3/2017
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-015-0357-2

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