nach oben

Artificial Intelligence Review

Erschienen in:

20.05.2021

Bee-inspired metaheuristics for global optimization: a performance comparison

verfasst von: Ryan Solgi, Hugo A. Loáiciga

Erschienen in: Artificial Intelligence Review | Ausgabe 7/2021

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Metaheuristics are widely applied to solve optimization problems. Numerous metaheuristic algorithms inspired by natural processes have been introduced in the past years. Studying and comparing the convergence of metaheuristics is helpful in future algorithmic development and applications. This study focuses on bee-inspired metaheuristics and identifies seven basic or root algorithms applied to solve continuous optimization problems. They are the bee system, mating bee optimization (MBO), bee colony optimization, bee evolution for genetic algorithms (BEGA), bee algorithm, artificial bee colony (ABC), and bee swarm optimization. The algorithms’ performances are evaluated with several benchmark problems. This study’s results rank the cited algorithms according to their convergence efficiency. The strengths and shortcomings of each algorithm are discussed. The ABC, BEGA, and MBO are the most efficient algorithms. This study’s results show the convergence rate among different algorithms varies, and evaluates the causes of such variation.

Vorheriger Artikel Over a decade of social opinion mining: a systematic review

Nächster Artikel Systematic reviews in sentiment analysis: a tertiary study

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

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!

Jetzt informieren

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!

Jetzt informieren

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!

Jetzt informieren

Fitness function refers to a penalized objective function, that is, the objective function with constraints added to it as penalty.

In this manuscript selections always are done with replacement.

This work applies uniform crossover in all algorithms in which the crossover function is used. The number and place of crossover points are random and uniformly distributed (Bozorg-Haddad et al. 2017a, b).

Abbass HA (2001) MBO: marriage in honey bees optimization a haplometrosis polygynous swarming approach. In: Proceedings of the 2001 congress on evolutionary computation (IEEE Cat. No.01TH8546), 27–30 May, Seoul, South Korea

Abbass H, Teo J (2003) A true annealing approach to the marriage in honey-bees optimization algorithm. Int J Comput Intell Appl 3(2):199–211CrossRef

Abualigah LMQ (2019) Feature selection and enhanced krill herd algorithm for text document clustering. Studies in computational intelligence. Springer, BerlinCrossRef

Abualigah L (2020) Multi-verse optimizer algorithm: a comprehensive survey of its results, variants, and applications. Neural Comput Appl 32:12381–12401CrossRef

Abualigah L, Diabat A (2020) A novel hybrid antlion optimization algorithm for multi-objective task scheduling problems in cloud computing environments. Clust Comput. https://doi.org/10.1007/s10586-020-03075-5CrossRef

Abualigah LMQ, Hanandeh ES (2015) Applying genetic algorithms to information retrieval using vector space model. Int J Comput Sci Eng Appl 5(1):19

Abualigah LM, Khader AT, Hanandeh ES (2017) A new feature selection method to improve the document clustering using particle swarm optimization algorithm. J Comput Sci 25:456–466CrossRef

Abualigah LM, Khader AT, abd Hanandeh ES (2018a) Hybrid clustering analysis using improved krill herd algorithm. Appl Intell 48:4047–4071CrossRef

Abualigah LM, Khader AT, Hanandeh ES (2018b) A combination of objective functions and hybrid krill herd algorithm for text document clustering analysis. Eng Appl Artif Intell 73:111–125CrossRef

Abualigah L, Diabat A, Geem ZW (2020a) A comprehensive survey of the harmony search algorithm in clustering applications. Appl Sci 10(11):3827CrossRef

Abualigah L, Shehab M, Alshinwan M, Mirjalili S, Elaziz MA (2020b) Ant lion optimizer: a comprehensive survey of its variants and applications. Arch Comput Methods Eng. https://doi.org/10.1007/s11831-020-09420-6CrossRef

Akbari R, Mohammadi A, Ziarati K (2010) A novel bee swarm optimization algorithm for numerical function optimization. Commun Nonlinear Sci Number Simulat 15:3142–3155MathSciNetMATHCrossRef

Ashghari S, Jafari Navimipour N (2019a) Cloud service composition using an inverted ant colony optimization algorithm. Int J Bio-Inspir Comput 13(4):257CrossRef

Ashghari S, Jafari Navimipour N (2019b) Resource discovery in the peer to peer networks using an inverted ant colony optimization algorithm. Peer Peer Netw Appl 12:129–142CrossRef

Aslan S (2019) A transition control mechanism for artificial bee colony (ABC) algorithm. Comput Intell Neurosci 2019:5012313CrossRef

Aslan S, Badem H, Karaboga D (2019) Improved quick artificial bee colony (iqABC) algorithm for global optimization. Soft Comput 23:13161–13182CrossRef

Banharnsakun A, Achalakul T, Sirinaovakul B (2011) The best-so-far selection in artificial bee colony algorithm. Appl Soft Comput 11:2888–2901CrossRef

Barker JSF (1958) Simulation of genetic systems by automatic digital computers. Aust J Biol Sci 11(4):603–612CrossRef

Box GEP (1957) Evolutionary operation: a method for increasing industrial productivity. Appl Stat 6(2):81–101CrossRef

Boyd S, Vandenberghe L (2004) Convex optimization. Cambridge Uni. Press, CambridgeMATHCrossRef

Bozorg-Haddad O, Afshar A, Marino MA (2006) Honey-bees mating optimization (HBMO) algorithm: a new heuristic approach for water resources optimization. Water Resour Manag 20:661–680CrossRef

Bozorg-Haddad O, Hoseini-Ghafari S, Solgi M, Loaiciga HA (2016a) Intermittent urban water supply with protection of consumer’s welfare. J Pipeline Syst Eng Pract 7(3):04016002CrossRef

Bozorg-Haddad O, Ghajarnia N, Solgi M, Loaiciga HA (2016b) A DSS based honey bee mating optimization (HBMO) algorithm for single- and multi-objective design of water distribution networks. In: Metaheuristic and optimization in civil engineering. Springer, Cham, pp 199–233

Bozorg-Haddad O, Ghajarnia N, Solgi M, Loaiciga HA, Marino MA (2017a) Multi-objective design of water distribution systems based on the fuzzy reliability index. J Water Supply Res Technol 66(1):36–48CrossRef

Bozorg-Haddad O, Solgi M, Loaiciga HA (2017b) Meta-heuristic and evolutionary algorithms for engineering optimization. Wiley, New YorkCrossRef

Bremermann HJ (1962) Optimization through evolution and recombination. In: Yovits MC, Jacobi GT, Goldstein GD (eds) Self-organized systems. Spartan Books, Washington

Celik Y, Ulker E (2013) An improved marriage in honey bees optimization algorithm for single objective constrained optimization. Sci World J 2013:370172CrossRef

Chen X, Tianfield H, Li K (2019) Self-adaptive differential bee colony algorithm for global optimization problem. Swarm Evol Comput 45:70–91CrossRef

Comellas F, Mrtinez-Navaro J (2009) Bumblebees: a multiagent combinatorial optimization algorithm inspired by social insect behavior. In: Proceedings of the first ACM/SIGEVO summit on genetic evolutionary computation, 12–14 June, Shanghai, China

Cui L, Li G, Luo Y, Chen F, Ming Z, Lu N, Lu J (2018) An enhanced artificial bee colony algorithm with dual-population framework. Swarm Evol Comput 43:184–206CrossRef

Darwish A, Hassanien AE, Das S (2019) A survey of swarm and evolutionary computing approaches for deep learning. Artif Intell Rev 53:1767–1812CrossRef

De Jong K, Fogel DB, Schwefel HP (1997) A history of evolutionary computation. In: Back T, Fogel DB, Michalewicz Z (eds) Handbook of evolutionary computation. IOP publishing Ltd and Oxford University Press, Oxford

Dereli S, Koker R (2019) A metaheuristic proposal for inverse kinematics solution of 7-DOF serial robotic manipulator: quantum behaved particle swarm algorithm. Artif Intell Rev 53:949–964CrossRef

Dorigo M, Maniezzo V, Colorni A (1991) Positive feedback as a search strategy. Dipartimento di Elettronica, Politecnico di Milano, Milano, Technical Report No 91-016

Dorigo M, Maniezzo V, Colorni A (1996) The ant system: Optimization by a colony of cooperating ants. IEEE Trans Syst Man Cybern Part B 26(1):29–42CrossRef

Eusuff MM, Lansey KE (2003) Application of the shuffled frog leaping algorithm for the optimization of a general large-scale water supply system. Water Resour Manag 23(4):797–823

Fogel LJ, Owens AJ, Walsh MJ (1966) Artificial intelligence through simulated evolution. Wiley, New YorkMATH

Friedberg RM (1958) A learning machine: part I. IBM J Res Dev 2(1):2–13CrossRef

Gao WF, Liu SY, Huang LL (2013a) A novel artificial bee colony algorithm based on modified search equation and orthogonal learning. IEEE Trans Cybern 43(3):1011CrossRef

Gao W, Liu S, Huang L (2013b) A global best artificial bee colony algorithm for global optimization. J Comput Appl Math 236:2741–2753MathSciNetMATHCrossRef

Gao WF, Huang LL, Liu SY, Dai C (2015) Artificial bee colony algorithm based on information learning. IEEE Trans Cybern 45(12):2827CrossRef

Glover F (1986) Future paths for integer programming and links to artificial intelligence. Comput Oper Res 13:533–549MathSciNetMATHCrossRef

Gupta S, Deep K (2019) Hybrid sin cosine artificial bee colony algorithm for global optimization and image segmentation. Neural Comput Appl 32:9521–9543CrossRef

Hajimirzaei B, Jafari Navimipour N (2019) Intrusion detection for cloud computing using neural networks and artificial bee colony optimization algorithm. ICT Express 5(1):56CrossRef

Hillier FS, Liberman GJ (1995) Introduction to operations research, 6th edn. McGraw-Hill, New York

Holland JH (1967) Nonlinear environments permitting efficient adaptation. Computer and information sciences II. Academic Press Inc, New YorkMATH

Holland JH (1975) Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor

Hooke R, Jeeves TA (1961) Direct search solution of numerical and statistical problems. J ACM 8(2):212–229MATHCrossRef

Hussein WA, Sahran S, Sheikh Abdullah SNH (2016) The variants of the bees algorithm (BA): s survey. Artif Intell Rev 47(1):67CrossRef

Jong GJ, Horng GJ (2017) A novel queen honey bee migration (QHBM) algorithm for sink repositioning in wireless sensor network. Wirel Pers Commun 95:3209–3232CrossRef

Jung SH (2003) Queen-bee evolution for genetic algorithms. Electron Lett 39(6):575CrossRef

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

Karaboga D, Akay B (2009) A survey: algorithms simulating bee swarm intelligence. Artif Intell Rev 31:61–85CrossRef

Karaboga D, Basturk B (2008) On the performance of artificial bee colony (ABC) algorithm. Appl Soft Comput 8:687–698CrossRef

Karaboga D, Gorkemli B, Ozturk C, Karaboga N (2012) A comprehensive survey: artificial bee colony (ABC) algorithm and applications. Artif Intell Rev 42:21–57CrossRef

Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceeding of international conference on neural networks, Perth, Australia, November 27 to December 1, Institute of Electrical and Electronics Engineers (IEEE), Piscataway, NJ, pp 1942–1948

Khan L, Ullah I, Saeed T, Lo KL (2010) Virtual bees algorithm based design of damping control system for TCSC. Aust J Basic Appl Sci 4(1):1–18

Kirkpatrick S, Gelatte CD, Vecchi MP (1983) Optimization by simulated annealing. Science 220(4589):671–680MathSciNetMATHCrossRef

Koc (2010) The bees algorithm theory, improvements and applications. PhD thesis, Cardiff University, Cardiff, UK

Kruger TJ, Davidovic T, Teodorovic D, Selmic M (2016) The bee colony optimization algorithm and its convergence. Int J Bio Inspir Comput 8(5):340CrossRef

Lucic P (2002) Modeling transportation problems using concepts of swarm intelligence and soft computing. PhD thesis, Virginia Polytechnic Institute and State University, Virginia, USA

Mernik M, Liu SH, Karaboga D, Crepinsek M (2015) On clarifying misconceptions when comparing variants of the artificial bee colony algorithm by offering a new implementation. Inf Sci 291:115–127MathSciNetMATHCrossRef

Ming H, Baohui J, Xu L (2010) An improved bee evolutionary genetic algorithm. In: IEEE international conference on intelligent computation and intelligent systems, 29–31 October, Xiamen, China

Molga M, Smutnicki C (2005) Test functions for optimization needs. http://www.zsd.ict.pwr.wroc.pl/files/docs/functions.pdf. Accessed Nov 2020

Moradipari A, Alizadeh M (2018) Pricing differentiated services in an electric vehicle public charging station network. In: 57th IEEE conference on decision and control (CDC), December 17–19, FL, USA

Nasrinpour HR, Bavani MA, Teshnehlab M (2017) Grouped bees algorithm: a grouped version of the bees algorithm. Computers 6(1):5CrossRef

Nikolic M, Teodorovic D (2013) Empirical study of the bee colony optimization (BCO) algorithm. Expert Syst Appl 40:4609–4620CrossRef

Panahi V, Jafari Navimipour N (2019) Join query optimization in the distributed database system using an artificial bee colony algorithm and genetic operators. Concurr Comput Pract Exp 31(17):e5218CrossRef

Pham DT, Darwish AH (2008) Fuzzy selection of local search sites in the bees algorithm. In: Pham DT, Eldukhri EE, Soroka AJ (eds) Innovative production machines and systems. Cardiff University, Cardiff

Pham DT, Ghanbarzadeh A, Koc E, Otri S, Rahim S, Zaidi M (2005) Bee algorithm a novel approach to function optimization. Technical Note: MEC 0501, Cardiff University, Cardiff, UK

Pham QT, Pham DT, Castellani M (2011) A modified bees algorithm and a statistics-based method for tuning its parameters. Proc Inst Mech Eng Part I J Syst Control Eng 226:287–301

Poolsamran P, Thammano A (2011) A modified marriage in honey-bee optimization for function optimization problems. Procedia Comput Sci 6:335–342CrossRef

Qin Q, Cheng S, Zhang Q, Li L, Shi Y (2015) Artificial bee colony algorithm with time varying strategy. In: Discrete Dynamics in Nature and Society, 2015, 674595

Quijano N, Passino KM (2010) Honey bee social foraging algorithms for resource allocation: theory and Application. Eng Appl Artif Intell 23(6):845CrossRef

Rabe M, Deininger M (2012) State of art and research demands for simulation modeling of green supply chains. Int J Autom Technol 6(3):296CrossRef

Rechenberg I (1965) Cybernetic solution path of an experimental problem. Royal Aircraft Establishment Library Translation 1122

Rudolph G (2012) Stochastic convergence. In: Rozenberg G, Back T, Kok JN (eds) Handbook of natural computing. Springer, Berlin, pp 847–869CrossRef

Sato T, Hagiwara M (1997) Bee system: finding solution by a concentrated search. In: IEEE international conference on systems, man, and cybernetics. computational cybernetics and simulation, 12–15 October, Orlando, FL, USA

Solgi M, Bozorg-Haddad O, Seifollahi Aghmiuni S, Ghasemi-Abiazani P, Loaiciga HA (2016) Optimal operation of water distribution networks under water shortage considering water quality. J Pipeline Syst Eng Pract 7(3):04016005CrossRef

Solgi M, Bozorg-Haddad O, Loaiciga HA (2017) The enhanced honey-bee mating optimization algorithm for water resources optimization. Water Resour Manag 31:885–901CrossRef

Sorensen K, Sevaux M, Glover F (2017) A history of metaheuristics. In: Marti R, Pardalos P, Resende M (eds) Handbook of heuristics. Springer, Berlin

Starke S, Hendrich N, Zhang J (2019) Memetic evolution for genetic full-body inverse kinematics in robotics and animation. IEEE Trans Evol Comput 23(3):406CrossRef

Storn R, Price K (1997) Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11(4):341–359MathSciNetMATHCrossRef

Tsai P, Chu SC, Pan JS (2009) Enhanced artificial bee colony optimization. Int J Innov Comput Inf Control 5(12):5081

Wang B, Wang L (2012) A novel artificial bee colony algorithm for numerical function optimization. In: Fourth international conference on computational and information sciences, 17–19 August, Chongqing, China

Wedde HF, Farooq M, Zhang Y (2004) BeeHive: an efficient fault-tolerant routing algorithm inspired by honey bee behavior. In: Dorigo M, Birattari M, Blum C, Gambardella LM, Mondada F, Stutzle Th (eds) Ant colony optimization and swarm intelligence. Springer, Berlin

Xiang W, An M (2013) An efficient and robust artificial bee colony algorithm for numerical optimization. Comput Oper Res 40:1256–1265MathSciNetMATHCrossRef

Xu C, Zhang Q, Li J, Zhao X (2008) A bee swarm genetic algorithm for the optimization of DNA encoding. In: The 3rd international conference on innovative computing information and control (ICICIC’08), 18–20 June, Dalian, China

Xu B, Zhang M, Browne WM, Yao X (2016) A survey on evolutionary computation approached to feature selection. IEEE Trans Evol Comput 20(4)

Yang XS (2011) Metaheuristic optimization: algorithm analysis and open problems. In: Pardalos PM, Rebennack S (eds) SEA 2011, LNCS 6630. Springer, Berlin

Yang C, Chen J, Tu X (2007) Algorithm of fast marriage in honey bees optimization and convergence analysis. In: Proceedings of IEEE international conference on automation and logistics, August 18–21, Jinan, China

Yuce B, Packianather MS, Mastrocinque E, Pham DT, Lambiase A (2013) Honey bees inspired optimization method: the bees algorithm. Insects 4:646–662CrossRef

Zanbouri K, Jafari Navimipour N (2019) A cloud service composition method using a trust-based clustering algorithm and honeybee mating optimization algorithm. Int J Commun Syst 33:e4259CrossRef

Zhu G, Kwong S (2010) Gbest-guided artificial bee colony algorithm for numerical function optimization. Appl Math Comput 217:3166–3172MathSciNetMATH

Titel: Bee-inspired metaheuristics for global optimization: a performance comparison
verfasst von: Ryan Solgi
Hugo A. Loáiciga
Publikationsdatum: 20.05.2021
Verlag: Springer Netherlands
Erschienen in: Artificial Intelligence Review / Ausgabe 7/2021
Print ISSN: 0269-2821
Elektronische ISSN: 1573-7462
DOI: https://doi.org/10.1007/s10462-021-10015-1

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Weitere Artikel der Ausgabe 7/2021

Underwater image enhancement: a comprehensive review, recent trends, challenges and applications

Systematic reviews in sentiment analysis: a tertiary study

Deep learning for Alzheimer prediction using brain biomarkers

Six application scenarios of artificial intelligence in the precise diagnosis and treatment of liver cancer

An In-ad contents-based viewability prediction framework using Artificial Intelligence for Web Ads

Evaluating logic gate constraints in local search for structured satisfiability problems

Premium Partner