The International Arab Journal of Information Technology (IAJIT)

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Modified Bee ColonyOptimizationfor the Selection of DifferentCombinationof Food Sources

There is a trend in the scientific community to model and solve complex optimization process byemploying natural metaphors. In this area,Artificial Bee Colony optimization (ABC)tries tomodel naturalbehaviourof realhoneybeesinfood foraging.ABCalgorithmis an optimizationalgorithmbasedontheintelligentbehaviourofhoneybee swarm.In this work, ABC is used for solving multivariablefunctions withdifferent combinations of them.That is, all theroutes areidentified to the beesand using allthe possible combinations,the outputsare measured.Based on the output theoptimum valueis selected.


[1]BenatchbaK.,AdmaneL.,andKoudilM., UsingBeesto Solve aData Mining Problem Expressedas aMax-Setone,Artificial IntelligenceandKnowledge Engineering ApplicationsaBio Inspired Approach, in Proceedings of the 1stInternational Work Conferenceon theInter PlaybetweenNatural andArtificial Computation,Laspalmas, Spain, pp. 212-220,2005.

[2]DebK.,Optimization for Engineering Design Algorithms and Examples, Prentice Hall India, 2000.

[3]DilayG., Ali H.,andKamil D., Evaluation of the Performances of Artificial Bee Colony and Invasive Weed Optimization Algorithms on the Modified Bench mark Functions, Academic ResearchInternational,vol.2,no. 3, pp.142- 147,2012.

[4]DriasH.,SedagS.,andYahiS., Cooperative SwarmforSolvingtheMaximum Weighted Satisfiability Problem,Computational Intelligenceand Bioinspired Systems, in Proceedings of the 8thInternational Workshopon Artificial NeuralNetworks,Spain,pp.318-325, 2005.

[5]FahimehA. andMohammed R., LearningBees Algorithm forOptimization, inProceedings of InternationalConference onInformationand Intelligent Computing,pp. 115-122,2011. 618The International Arab Journal of Information Technology, Vol. 13, No. 6, November 2016

[6]KarabogaD. andAkayB., A Comparative Study ofArtificial Bee ColonyAlgorithm, Applied Mathematics andComputation on Science Direct,vol.214,no.1,pp.108-132, 2009.

[7]Karaboga D. and Bastruk B., APowerfuland Efficient AlgorithmforNumerical Function Optimization:Artificial Bee Colony Algorithm, Journal of Global Optimization,vol.39,no. 3, pp. 459-471, 2007.

[8]LucicP. andTeodorovicD., Transportation Modeling:AnArtificial Life Approach, in Proceedings of the14thIEEE International Conference on Tools withArtificial Intelligence, Washington, pp.216-223,2002.

[9]SengPohL., PerformanceofDifferent TechniquesAppliedinGeneticAlgorithm towards Benchmark Functions, Intelligence and Database Systems, ALecture NotesinComputer Science,vol.7802, pp.255-264,2013.

[10]StanarevicN.,Tuba M.,andBecaninN., ModifiedArtificial Bee Colony Algorithmfor Constrained Problems Optimization, InternationalJournalofMathematical Models andMethods in Applied Sciences,vol. 5,no.3, pp. 644-651,2011.

[11]TeodorovicD.andDell orcoM., Bee Colony Optimization-a CooperativeLearning Approach toComplex Transportation Problems, in Proceedingsof the10thEWGTMeeting, Ponzan, pp.13-16, 2005.

[12]Teodorovic D., Transport Modeling by Multi- Agent Systems:A Swarm Intelligent Approach, Transportation Planning and Technology,vol. 26,no. 4, pp. 289-312, 2003.

[13]TereshkoV. andLeeT., HowInformation Mapping Patterns Determine Foraging Behaviour ofaHoney Bee Colony, Open Systems and Information Dynamics,vol.9,no. 2,pp.181-193, 2002.

[14]TereshkoV.andLoengarorA., Collective Decision-Makingin HoneyBeeForaging Dynamics, availableat; http://cis.uws.ac.uk/research/journal/V9/V9N3/be es.pdf,last visited2005.

[15]Tereshko V., Reaction-Diffusion Model of Honey BeesColony s Foraging Be-Haviour, in Proceedings of the 6thInternational Conference on Parallel Problem Solving from Nature, pp. 807-816, Berlin, 2000.

[16]WeedleH., FarooqM.andZhangY., BeeHive: AnEfficient Fault-Tolerant Routing Algorithm InspiredbyHoneybeeBehavior,Ant Colony OptimizationandSwarmIntelligence, in Proceedingsofthe4thInternational Workshop, Brussels, Belgium,pp 83-94,2004.

[17]YangX., EngineeringOptimizationsviaNature Inspired Virtual Bee Algorithms, in Proceedings of the1stInternational Work-Conference on the Interplay Between Natural and Artificial Computation,Spain,pp.317-323, Springer, 2005.

[18]Al-Khanjari Z.,KuttiS.,andHatem M., An Extended E-learning System Architecture: Integrating Software Tools within the E-learning Portal, TheInternational Arab Journal of Information Technology,vol.3,no.1,pp. 75-81, 2006. SaravanamoorthiMoorthiis working as an Assistant professor in Senior Grade in Bannari Amman Institute of Technology, India. He has completed MScin Mathematics inBharathiar University, India. He has more than a decade of teaching experience in various Engineering colleges in India. He has done his research in the area ofoptimization techniquesand his research work is Performance Evaluation of Sugar Industry using Optimization Techniques. He has published seven papers in various International Journalsin the area related to the optimization techniques.