Top

Soft Computing

Published in:

11-05-2016 | Methodologies and Application

Shuffled artificial bee colony algorithm

Authors: Tarun Kumar Sharma, Millie Pant

Published in: Soft Computing | Issue 20/2017

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

In this study, we have introduced a hybrid version of artificial bee colony (ABC) and shuffled frog-leaping algorithm (SFLA). The hybrid version is a two-phase modification process. In the first phase to increase the global convergence, the initial population is produced using randomly generated and chaotic system, and then in the second phase to balance two antagonist factors, i.e., exploration and exploitation capabilities, population is portioned into two groups (superior and inferior) based on their fitness values. ABC is applied to the first group, whereas SFLA is applied to the second group of population. The proposed version is named as Shuffled-ABC. The proposal is implemented and tested on constrained benchmark consulted from CEC 2006 and five chemical engineering problems where constraints are handled using penalty function methods. The simulated results illustrate the efficacy of the proposal.

previous article Fuzzy soft uniform spaces

next article Development supporting framework of architectural descriptions using heavy-weight ontologies with fuzzy-semantic similarity

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Adjiman CS, Androulakis IP, Floudas CA (1998) A global optimization method, alphaBB, for general twice-differentiable constrained NLPs: II–implementation and computational results. Comput Chem Eng 22:1159–1179CrossRef

Alatas B (2010) Chaotic bee colony algorithms for global numerical optimization. Expert Syst Appl 37:5682–5687CrossRef

Al-Salamah M (2015) Constrained binary artificial bee colony to minimize the makespan for single machine batch processing with non-identical job sizes. Appl Soft Comput 29:379–385CrossRef

Alvarado-Iniesta A, Garcia-Alcaraz JL, Rodriguez-Borbon MI, Maldonado A (2013) Optimization of the material flow in a manufacturing plant by use of artificial bee colony algorithm. Expert Syst Appl 40(12):4785–4790CrossRef

Babaeizadeh S, Ahmad R (2016) An improved artificial bee colony algorithm for constrained optimization. Res J Appl Sci 11(1):14–22

Barton R (1990) Chaos and fractals. Math Teach 83:524–529

Brajevic I (2015) Crossover-based artificial bee colony algorithm for constrained optimization problems. Neural Comput Appl 26:1587–1601CrossRef

Chidambaram C, Lopes HS (2010) An improved artificial bee colony algorithm for the object recognition problem in complex digital images using template matching. Int J Nat Comput Res IJNCR 1(2):54–70. doi:10.4018/jncr.2010040104 CrossRef

Črepinšek M, Liu S-H, Mernik M (2013) Exploration and exploitation in evolutionary algorithms: a survey. ACM Comput Surv 45(3):1–33. doi:10.1145/2480741.2480752

Das S, Biswas S, Kundu S (2013) Synergizing fitness learning with proximity-based food source selection in artificial bee colony algorithm for numerical optimization. Appl Soft Comput 13(12):4676–4694CrossRef

Deb K (2000) An efficient constraint handling method for genetic algorithms. Comput Methods Appl Mech Eng 186:311–338CrossRefMATH

Dorigo M, Stutzle T (2004) Ant colony optimization. MIT Press, CambridgeMATH

Edgar TF, Himmelblau DM, Lasdon L (1998) Optimization of chemical processes, 2nd edn. Mcgraw-Hill, New York

Eusuff M, Lansey K, Pasha F (2006) Shuffled frog-leaping algorithm: a memetic meta-heuristic for discrete optimization. Eng Optim 38(2):129–154MathSciNetCrossRef

Fister I, Fister I Jr, Brest J, Zumer V (2012) Memetic articial bee colony algorithm for large-scale global optimization. In: Proceedings of IEEE CEC—2012, Brisbane, Australia

Fister I, Perc M, Kamal SM (2015a) A review of chaos-based firefly algorithms. Appl Math Comput 252:155–165MathSciNetMATH

Fister I, Strnad D, Yang X-S, Fister I Jr (2015b) Adaptation and hybridization in nature-inspired algorithms. In: Adaptation and Hybridization in Computational Intelligence. Springer, pp 3–50

Floudas CA, Pardalos PM (1990) A collection of test problems for constrained global optimization algorithms. Lecture notes in computer science, vol 455. Springer, Berlin

Goldberg DE (1989) Genetic algorithms in search. Optimization and machine learning, Addison-Wesley, BostonMATH

Hansen (2006) Compilation of results on the 2005 CEC benchmark function set. May 4, 2006. http://www.ntu.edu.sg/home/epnsugan/index_files/CEC-05/compareresults.pdf

Kang F, Li J, Li H (2013a) Artificial bee colony algorithm and pattern search hybridized for global optimization. Appl Soft Comput 13(4):1781–1791CrossRef

Kang F, Li J, Ma Z (2013b) An artificial bee colony algorithm for locating the critical slip surface in slope stability analysis. Eng Optim 45(2):207–223MathSciNetCrossRef

Kang F, Xu Q, Li J (2016) Slope reliability analysis using surrogate models via new support vector machines with swarm intelligence. Appl Math Model. doi:10.1016/j.apm.2016.01.050 MathSciNet

Kang F, Li J (2015) Artificial bee colony algorithm optimized support vector regression for system reliability analysis of slopes. J Comput Civ Eng. doi:10.1061/(ASCE)CP.1943-5487.0000514, 04015040

Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. Erciyes University, Technical Report-TR06, Kayseri, Turkey

Karaboga D, Ozturk C, Karaboga N, Gorkemli B (2012) Artificial bee colony programming for symbolic regression. Inf Sci 209(20):1–15CrossRef

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

Karaboga D, Basturk B (2007) Artificial bee colony (ABC) optimization algorithm for solving constrained optimization problems. In: Foundations of fuzzy logic and soft computing, 12th International Fuzzy Systems Association, World Congress, IFSA 2007 Lecture notes in artificial intelligence, vol 4529, pp 789–798

Karaboga D, Basturk B (2007) Artificial bee colony (ABC) optimization algorithm for solving constrained optimization. In Proceedings of IFSA 2007. LNAI, vol 4529, pp 789–798

Kennedy J, Eberhart R (1995) Particle swarm optimization. Proceedings of the IEEE international conference neural networks 4:1942–1948CrossRef

Kıran MS, Fındık O (2015) A directed artificial bee colony algorithm. Appl Soft Comput 26:454–462CrossRef

Liang JJ, Runarsson TP, Mezura-Montes E, Clerc M, Suganthan PN, Coello CAC, Deb K (2006) Problem definitions and evaluation criteria for the CEC special session on constrained real-parameter optimization, Technical Report, Nanyang Technological University. Singapore. http://www.ntu.edu.sg/home/EPNSugan

Li X, Yin M (2014) Self-adaptive constrained artificial bee colony for constrained numerical optimization. Neural Comput Appl. 24(3–4):723–734CrossRef

Mezura-Montes E, Cetina-Domı’nguez O (2012) Empirical analysis of a modified artificial bee colony for constrained numerical optimization. Appl Math Comput 218(22):10943–10973MathSciNetMATH

Mezura-Montes E, Veåazquez-Reyes J, Coello Coello CA (2006) Modified differential evolution for constrained optimization. In Proceedings of IEEE Congress on Evolutionary Computation, Canada, pp 25–32

Munoz-Zavala AE, Hernandez-Aguirre A, Villa-Diharce ER, Botello-Rionda S (2006) PESO+ for constrained optimization. In: Proceedings of IEEE congress on evolutionary computation Canada, pp 231–238

Passino KM (2002) Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Syst Mag 22(3):52–67CrossRef

Problem Definitions and Evaluation Criteria for the CEC (2006) Special session on constrained real-parameter optimization. Nanyang Technological University, Singapore

Sharma TK, Pant M, Neri F (2014) Changing factor based food sources in artificial bee colony. In Proceedings of IEEE symposium on swarm intelligence (SIS), 1–7, (2014) Orlando. Florida, USA

Sharma TK, Pant M (2013) Enhancing the food locations in an artificial bee colony algorithm. Soft Comput 17(3):1939–1965CrossRef

Simon D (2008) Biogeography-based optimization. IEEE Trans Evol Comput 12:702–713CrossRef

Subotic M (2011) Artificial bee colony algorithm with multiple onlookers for constrained optimization problems. In: Proceedings of the European computing conference, pp 251–256

Taherdangkoo M (2014) Skull removal in MR images using a modified artificial bee colony optimization algorithm. Technol Health Care 22(5):775–784

Xu Y, Fan P, Yuan L (2013) A simple and efficient artificial bee colony algorithm. Math Probl Eng 2013:1–9

Yang X-S, Deb S (2009) Cuckoo search via Lévy flights. In: Nature & biologically inspired computing, 2009. NaBIC 2009. World Congress on. IEEE, Coimbatore, pp 210–214

Yang D, Liu Y, Li S, Li X, Ma L (2015) Gear fault diagnosis based on support vector machine optimized by artificial bee colony algorithm. Mech Mach Theory 90:219–229CrossRef

Zavala AEM, Aguirre AH, Diharce ERV (2005) Constrained optimization via particle evolutionary swarm optimization algorithm (PESO). In: Proceedings of the 2005 conference on genetic and evolutionary computation (GECCO’05), pp 209–216

Zhang X, Fong KF, Yuen SY (2013) A novel artificial bee colony algorithm for HVAC optimization problems. HVAC&R Res 19(6):715–731

Title: Shuffled artificial bee colony algorithm
Authors: Tarun Kumar Sharma
Millie Pant
Publication date: 11-05-2016
Publisher: Springer Berlin Heidelberg
Published in: Soft Computing / Issue 20/2017
Print ISSN: 1432-7643
Electronic ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-016-2166-2

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Other articles of this Issue 20/2017

Adaptable key-policy attribute-based encryption with time interval

Data-based multiple criteria decision-making model and visualized monitoring of urban drinking water quality

A set-cover-based approach for the test-cost-sensitive attribute reduction problem

Fuzzy soft uniform spaces

An improved immune system-inspired routing recovery scheme for energy harvesting wireless sensor networks

Improved immune computation for high-precision face recognition

Premium Partner