A survey: algorithms simulating bee swarm intelligence

Swarm intelligence is an emerging area in the field of optimization and researchers have developed various algorithms by modeling the behaviors of different swarm of animals and insects such as ants, termites, bees, birds, fishes. In 1990s, Ant Colony Optimization based on ant swarm and Particle Swarm Optimization based on bird flocks and fish schools have been introduced and they have been applied to solve optimization problems in various areas within a time of two decade. However, the intelligent behaviors of bee swarm have inspired the researchers especially during the last decade to develop new algorithms. This work presents a survey of the algorithms described based on the intelligence in bee swarms and their applications.

[1]  Barry J. Adams,et al.  Optimum rehabilitation strategy of water distribution systems using the HBMO algorithm , 2008 .

[2]  Rosni Abdullah,et al.  Protein Conformational Search Using Bees Algorithm , 2008, 2008 Second Asia International Conference on Modelling & Simulation (AMS).

[3]  Israel A. Wagner,et al.  Discrete bee dance algorithm for pattern formation on a grid , 2003, IEEE/WIC International Conference on Intelligent Agent Technology, 2003. IAT 2003..

[4]  Max Donath,et al.  American Control Conference , 1993 .

[5]  Reginald L. Walker,et al.  Honeybee search strategies: adaptive exploration of an information ecosystem , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[6]  Valery Tereshko,et al.  Phase Transitions and Bistability in Honeybee Foraging Dynamics , 2008, Artificial Life.

[7]  Dervis Karaboga,et al.  AN IDEA BASED ON HONEY BEE SWARM FOR NUMERICAL OPTIMIZATION , 2005 .

[8]  Karl Tuyls,et al.  Bee behaviour in multi-agent systems: a bee foraging algorithm , 2005 .

[9]  Horst F. Wedde,et al.  A novel class of multi-agent algorithms for highly dynamic transport planning inspired by honey bee behavior , 2007, 2007 IEEE Conference on Emerging Technologies and Factory Automation (EFTA 2007).

[10]  T. Niknam,et al.  A Hybrid Algorithm Based on HBMO and Fuzzy Set for Multi-Objective Distribution Feeder Reconfiguration , 2008 .

[11]  M. Dorigo,et al.  1 Positive Feedback as a Search Strategy , 1991 .

[12]  Xuyan Tu,et al.  Algorithm of Fast Marriage in Honey Bees Optimization and Convergence Analysis , 2007, 2007 IEEE International Conference on Automation and Logistics.

[13]  Dervis Karaboga,et al.  Artificial Bee Colony (ABC) Optimization Algorithm for Training Feed-Forward Neural Networks , 2007, MDAI.

[14]  Duc Truong Pham,et al.  APPLICATION OF THE BEES ALGORITHM TO THE TRAINING OF RADIAL BASIS FUNCTION NETWORKS FOR CONTROL CHART PATTERN RECOGNITION , 2006 .

[15]  S. Hemamalini,et al.  Emission constrained economic dispatch with valve-point effect using particle swarm optimization , 2008, TENCON 2008 - 2008 IEEE Region 10 Conference.

[16]  T. Seeley,et al.  Group decision making in nest-site selection by honey bees , 2004 .

[17]  Dušan Teodorović,et al.  Mitigating Traffic Congestion: Solving the Ride-Matching Problem by Bee Colony Optimization , 2008 .

[18]  H A Abbass,et al.  MARRIAGE IN HONEY-BEE OPTIMIZATION (MBO): A HAPLOMETROSIS POLYGYNOUS SWARMING APPROACH , 2001 .

[20]  Daniel A. Ashlock,et al.  Simulation of floral specialization in bees , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[21]  Li-Pei Wong,et al.  A Bee Colony Optimization Algorithm for Traveling Salesman Problem , 2008, 2008 Second Asia International Conference on Modelling & Simulation (AMS).

[22]  D.T. Pham,et al.  Application of the Bees Algorithm to the Training of Learning Vector Quantisation Networks for Control Chart Pattern Recognition , 2006, 2006 2nd International Conference on Information & Communication Technologies.

[23]  Horst F. Wedde,et al.  Highly Dynamic and Adaptive Traffic Congestion Avoidance in Real-Time Inspired by Honey Bee Behavior , 2007, PEARL.

[24]  R. Srinivasa Rao,et al.  Optimization of Distribution Network Configuration for Loss Reduction Using Artificial Bee Colony Algorithm , 2008 .

[25]  Babak Amiri,et al.  INTEGRATION OF SELF ORGANIZING FEATURE MAPS AND HONEY BEE MATING OPTIMIZATION ALGORITHM FOR MARKET SEGMENTATION , 2007 .

[26]  D. Pham,et al.  THE BEES ALGORITHM, A NOVEL TOOL FOR COMPLEX OPTIMISATION PROBLEMS , 2006 .

[27]  Ji Young Lee,et al.  Multi-objective Environmental/Economic Dispatch Using the Bees Algorithm with Weighted Sum , 2008 .

[28]  Xuyan Tu,et al.  Algorithm of Marriage in Honey Bees Optimization Based on the Wolf Pack Search , 2007, The 2007 International Conference on Intelligent Pervasive Computing (IPC 2007).

[29]  Ajith Abraham,et al.  Stigmergic Optimization (Studies in Computational Intelligence) , 2006 .

[30]  Christian Blum,et al.  Ant colony optimization: Introduction and recent trends , 2005 .

[31]  Horst F. Wedde,et al.  BeeHiveAIS: A Simple, Efficient, Scalable and Secure Routing Framework Inspired by Artificial Immune Systems , 2006, PPSN.

[32]  Duc Truong Pham,et al.  Simultaneous Feature Selection and Parameters Optimization for SVM by Immune Clonal Algorithm , 2005, ICNC.

[33]  Horst F. Wedde,et al.  BeeHiveGuard: A Step Towards Secure Nature Inspired Routing Algorithms , 2006, EvoWorkshops.

[34]  Chin Soon Chong,et al.  Using A Bee Colony Algorithm For Neighborhood Search In Job Shop Scheduling Problems , 2007 .

[35]  Lale Özbakır,et al.  Artificial Bee Colony Algorithm and Its Application to Generalized Assignment Problem , 2007 .

[36]  Habiba Drias,et al.  Cooperative Bees Swarm for Solving the Maximum Weighted Satisfiability Problem , 2005, IWANN.

[37]  Qiang Zhang,et al.  A Bee Swarm Genetic Algorithm for the Optimization of DNA Encoding , 2008, 2008 3rd International Conference on Innovative Computing Information and Control.

[38]  Duc Truong Pham,et al.  PRELIMINARY DESIGN USING THE BEES ALGORITHM , 2007 .

[39]  Horst F. Wedde,et al.  The wisdom of the hive applied to mobile ad-hoc networks , 2005, Proceedings 2005 IEEE Swarm Intelligence Symposium, 2005. SIS 2005..

[40]  Giovanni Michele Bianco,et al.  Getting inspired from bees to perform large scale visual precise navigation , 2004, 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566).

[41]  Sishaj P. Simon,et al.  Dynamic economic dispatch using artificial bee colony algorithm for units with valve‐point effect , 2011 .

[42]  Horst F. Wedde,et al.  BeeAdHoc: an energy efficient routing algorithm for mobile ad hoc networks inspired by bee behavior , 2005, GECCO '05.

[43]  Yongquan Zhou,et al.  A Genetic Algorithm Based on Multi-bee population evolutionary for numerical optimization , 2008, 2008 7th World Congress on Intelligent Control and Automation.

[44]  Kerim Guney,et al.  Bees algorithm for design of dual-beam linear antenna arrays with digital attenuators and digital phase shifters , 2008 .

[45]  Anies Hannawati Purnamadjaja,et al.  Pheromone communication in a robot swarm: necrophoric bee behaviour and its replication , 2005, Robotica.

[46]  Hafiz Farooq Ahmad,et al.  Using Honey Bee Teamwork Strategy in Software Agents , 2006, 2006 10th International Conference on Computer Supported Cooperative Work in Design.

[47]  Panta Lucic,et al.  Transportation modeling: an artificial life approach , 2002, 14th IEEE International Conference on Tools with Artificial Intelligence, 2002. (ICTAI 2002). Proceedings..

[48]  Y. Yonezawa,et al.  Ecological algorithm for optimal ordering used by collective honey bee behavior , 1996, MHS'96 Proceedings of the Seventh International Symposium on Micro Machine and Human Science.

[49]  Masafumi Hagiwara,et al.  Bee System: Finding Solution by a Concentrated Search , 1998 .

[50]  Dervis Karaboga,et al.  A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm , 2007, J. Glob. Optim..

[51]  Prashant J. Shenoy,et al.  Agile dynamic provisioning of multi-tier Internet applications , 2008, TAAS.

[52]  Ali Maroosi,et al.  A honeybee-mating approach for cluster analysis , 2008 .

[53]  Dušan Teodorović,et al.  Swarm intelligence systems for transportation engineering: Principles and applications , 2008 .

[54]  R. Menzel,et al.  Spatial memory, navigation and dance behaviour in Apis mellifera , 2006, Journal of Comparative Physiology A.

[55]  K.M. Passino Systems Biology of Group Decision Making , 2006, 2006 14th Mediterranean Conference on Control and Automation.

[56]  M. Rashid,et al.  Honey bee foraging algorithm for multimodal & dynamic optimization problems , 2007, GECCO '07.

[57]  Pavol Návrat Bee Hive Metaphor for Web Search , 2006 .

[58]  Hyeong Soo Chang,et al.  Converging Marriage in Honey-Bees Optimization and Application to Stochastic Dynamic Programming , 2006, J. Glob. Optim..

[59]  Siddhartha Ghosh,et al.  Simple Model of Collective Decision Making during Nectar Source Selection by Honey Bees , 2005 .

[60]  Duc Truong Pham,et al.  OPTIMIZATION OF THE WEIGHTS OF MULTI-LAYERED PERCEPTIONS USING THE BEES ALGORITHM , 2006 .

[61]  D. Floreano,et al.  Division of labour and colony efficiency in social insects: effects of interactions between genetic architecture, colony kin structure and rate of perturbations , 2006, Proceedings of the Royal Society B: Biological Sciences.

[62]  Magdalene Marinaki,et al.  A Hybrid Clustering Algorithm Based on Honey Bees Mating Optimization and Greedy Randomized Adaptive Search Procedure , 2008, LION.

[63]  Karl Tuyls,et al.  A bee algorithm for multi-agent systems: Recruitment and navigation combined , 2007 .

[64]  P. Lucic,et al.  Bee Colony Optimization: Principles and Applications , 2006, 2006 8th Seminar on Neural Network Applications in Electrical Engineering.

[65]  Babak Amiri,et al.  A honeybee-mating approach for cluster analysis , 2008 .

[66]  Goran Z. Markovic,et al.  Routing and wavelength assignment in all-optical networks based on the bee colony optimization , 2007, AI Commun..

[67]  Thomas Bäck,et al.  Parallel Problem Solving from Nature — PPSN V , 1998, Lecture Notes in Computer Science.

[68]  Dusˇan Teodorovic,et al.  MODELING BY MULTI-AGENT SYSTEMS : A SWARM INTELLIGENCE APPROACH , 2003 .

[69]  Adnan Yazici,et al.  Computer and Information Sciences - ISCIS 2003 , 2003, Lecture Notes in Computer Science.

[70]  Horst F. Wedde,et al.  BeeHive: New Ideas for Developing Routing Algorithms Inspired by Honey Bee Behavior , 2005 .

[71]  Miguel A. Mariño,et al.  Honey-bee mating optimization (HBMO) algorithm in deriving optimal operation rules for reservoirs , 2008 .

[72]  Hussein A. Abbass,et al.  A True Annealing Approach to the Marriage in Honey-Bees Optimization Algorithm , 2003, Int. J. Comput. Intell. Appl..

[73]  Duc Truong Pham,et al.  Some applications of the bees algorithm in engineering design and manufacture , 2007 .

[74]  Pavol Návrat,et al.  Web Search Engine as a Bee Hive , 2006, 2006 IEEE/WIC/ACM International Conference on Web Intelligence (WI 2006 Main Conference Proceedings)(WI'06).

[75]  G. Robinson Regulation of division of labor in insect societies. , 1992, Annual review of entomology.

[76]  D. Karaboga,et al.  Artificial Bee Colony (ABC) Algorithm on Training Artificial Neural Networks , 2007, 2007 IEEE 15th Signal Processing and Communications Applications.

[77]  Omid Bozorg Haddad,et al.  Honey-Bees Mating Optimization (HBMO) Algorithm: A New Heuristic Approach for Water Resources Optimization , 2006 .

[78]  Q.Y. Jiang,et al.  A queen-bee evolution based on genetic algorithm for economic power dispatch , 2004, 39th International Universities Power Engineering Conference, 2004. UPEC 2004..

[79]  E. Nazemi,et al.  An Agent - Based Architecture with Centralized Management for a Distance Learning System , 2007, International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007).

[80]  Petar Ćurković,et al.  Honey-bees optimization algorithm applied to path planning problem , 2007 .

[81]  Bruce L. Golden,et al.  The Label-Constrained Minimum Spanning Tree Problem , 2008 .

[82]  Craig A. Tovey,et al.  On Honey Bees and Dynamic Server Allocation in Internet Hosting Centers , 2004, Adapt. Behav..

[83]  Mouloud Koudil,et al.  Using Bees to Solve a Data-Mining Problem Expressed as a Max-Sat One , 2005, IWINAC.

[84]  Karl Tuyls,et al.  Bee System with inhibition Pheromones , 2007 .

[85]  Muddassar Farooq,et al.  Vulnerability analysis and security framework (BeeSec) for nature inspired MANET routing protocols , 2007, GECCO '07.

[86]  Hafiz Farooq Ahmad,et al.  Honey Bee Teamwork Architecture in Multi-agent Systems , 2006, CSCWD.

[87]  Alan F. Murray,et al.  IEEE International Conference on Neural Networks , 1997 .

[88]  Habiba Drias,et al.  A selective approach to parallelise Bees Swarm Optimisation metaheuristic: application to MAX-W-SAT , 2007 .

[89]  Mohammad Fazle Azeem,et al.  A Novel Parent Selection Operator in GA for Tuning of Scaling Factors of FKBC , 2006, 2006 IEEE International Conference on Fuzzy Systems.

[90]  Tu Xu-yan Optimization of Ground Anti-aircraft Weapon System Networks Based on Direction Probability and Algorithm of Improved Marriage in Honey Bee Optimization , 2008 .

[91]  Indranil Gupta,et al.  A new class of nature-inspired algorithms for self-adaptive peer-to-peer computing , 2008, TAAS.

[92]  Masafumi Hagiwara,et al.  Bee System: finding solution by a concentrated search , 1997, 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation.

[93]  David J. T. Sumpter,et al.  What makes a honeybee scout? , 2007, Behavioral Ecology and Sociobiology.

[94]  Muddassar Farooq,et al.  A sense of danger: dendritic cells inspired artificial immune system for manet security , 2008, GECCO '08.

[95]  Walter C. Rothenbuhler,et al.  The Honey Bee, Apis mellifera , 1975 .

[96]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[97]  Mouloud Koudil,et al.  Using artificial bees to solve partitioning and scheduling problems in codesign , 2007, Appl. Math. Comput..

[98]  Jie Chen,et al.  Algorithm of Marriage in Honey Bees Optimization Based on the Nelder-Mead Method , 2007 .

[99]  Marco Dorigo,et al.  Ant colony optimization theory: A survey , 2005, Theor. Comput. Sci..

[100]  Muddassar Farooq,et al.  BeeSensor: A Bee-Inspired Power Aware Routing Protocol for Wireless Sensor Networks , 2009, EvoWorkshops.

[101]  Yongquan Zhou,et al.  A Novel Global Convergence Algorithm: Bee Collecting Pollen Algorithm , 2008, ICIC.

[102]  Troy Lee,et al.  How Information-Mapping Patterns Determine Foraging Behaviour of a Honey Bee Colony , 2002, Open Syst. Inf. Dyn..

[103]  Ji Young Lee,et al.  Multi-objective optimisation using the Bees Algorithm , 2010 .

[104]  Ian W. Marshall,et al.  Simple Model of Learning and Collective Decision Making during Nectar Source Selection by Honey Bees , 2005 .

[105]  A. Dornhaus,et al.  Task Selection in Honeybees - Experiments Using Multi-Agent Simulation , 1998 .

[106]  Hussein A. Abbass,et al.  A Monogenous MBO Approach to Satisfiability , 2001 .

[107]  D. Karaboga,et al.  On the performance of artificial bee colony (ABC) algorithm , 2008, Appl. Soft Comput..

[108]  Andrzej Jaszkiewicz,et al.  Advanced OR and AI methods in transportation , 2009, Eur. J. Oper. Res..

[109]  Valery Tereshko,et al.  Reaction-Diffusion Model of a Honeybee Colony's Foraging Behaviour , 2000, PPSN.

[110]  David S. Broomhead,et al.  Formalising the Link between Worker and Society in Honey Bee Colonies , 1998, MABS.

[111]  Xin-She Yang,et al.  Engineering Optimizations via Nature-Inspired Virtual Bee Algorithms , 2005, IWINAC.

[112]  Grosan Crina,et al.  Stigmergic Optimization: Inspiration, Technologies and Perspectives , 2006 .

[113]  Min Huang,et al.  A Beehive Algorithm Based QoS Unicast Routing Scheme with ABC Supported , 2007, APPT.

[114]  V. Tereshko,et al.  Collective Decision-Making in Honey Bee Foraging Dynamics , 2005 .

[115]  Gustavo Olague,et al.  The Honeybee Search Algorithm for Three-Dimensional Reconstruction , 2006, EvoWorkshops.

[116]  Muddassar Farooq,et al.  Formal Modeling of BeeAdHoc: A Bio-inspired Mobile Ad Hoc Network Routing Protocol , 2008, ANTS Conference.

[117]  Horst F. Wedde,et al.  BeeHive: Routing Algorithms Inspired by Honey Bee Behavior , 2005, Künstliche Intell..

[118]  Guy Theraulaz,et al.  Adaptive Task Allocation Inspired by a Model of Division of Labor in Social Insects , 1997, BCEC.

[119]  R. L. Walker Emulating the honeybee information sharing model , 2003, IEMC '03 Proceedings. Managing Technologically Driven Organizations: The Human Side of Innovation and Change (IEEE Cat. No.03CH37502).

[120]  Dušan Teodorović,et al.  Bee Colony Optimization – a Cooperative Learning Approach to Complex Transportation Problems , 2005 .

[121]  Yue Zhang,et al.  BeeHive: An Efficient Fault-Tolerant Routing Algorithm Inspired by Honey Bee Behavior , 2004, ANTS Workshop.

[122]  John Hallam,et al.  Bee-havior in a mobile robot: the construction of a self-organized cognitive map and its use in robot navigation within a complex, natural environment , 1993, IEEE International Conference on Neural Networks.

[123]  Malcolm Yoke-Hean Low,et al.  A Bee Colony Optimization Algorithm to Job Shop Scheduling , 2006, Proceedings of the 2006 Winter Simulation Conference.

[124]  Panta Lucic,et al.  Modeling Transportation Problems Using Concepts of Swarm Intelligence and Soft Computing , 2002 .

[125]  Hussein A. Abbass,et al.  An Annealing Approach to the Mating-Flight Trajectories in the Marriage in Honey Bees Optimization Algorithm , 2001 .

[126]  Duc Truong Pham,et al.  Using the Bees Algorithm to tune a fuzzy logic controller for a robot gymnast , 2007 .

[127]  Panta Lucic,et al.  Computing with Bees: Attacking Complex Transportation Engineering Problems , 2003, Int. J. Artif. Intell. Tools.

[128]  K.M. Passino,et al.  Honey Bee Social Foraging Algorithms for Resource Allocation, Part II: Application , 2007, 2007 American Control Conference.

[129]  Ali Karci Imitation of Bee Reproduction as a Crossover Operator in Genetic Algorithms , 2004, PRICAI.

[130]  Nurhan Karaboga,et al.  A new design method based on artificial bee colony algorithm for digital IIR filters , 2009, J. Frankl. Inst..

[131]  Anies Hannawati Purnamadjaja,et al.  Guiding robots’ behaviors using pheromone communication , 2007, Auton. Robots.

[132]  Mark M. Millonas,et al.  Swarms, Phase Transitions, and Collective Intelligence , 1993, adap-org/9306002.

[133]  Georgios Dounias,et al.  Honey Bees Mating Optimization Algorithm for the Vehicle Routing Problem , 2007, NICSO.

[134]  Dušan Teodorović,et al.  Vehicle Routing Problem With Uncertain Demand at Nodes: The Bee System and Fuzzy Logic Approach , 2003 .

[135]  K.M. Passino,et al.  Honey Bee Social Foraging Algorithms for Resource Allocation, Part I: Algorithm and Theory , 2007, 2007 American Control Conference.

[136]  A. Gupta,et al.  SWAN: A Swarm Intelligence Based Framework for Network Management of IP Networks , 2007, International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007).

[137]  Craig Tovey,et al.  From honeybees to Internet servers: biomimicry for distributed management of Internet hosting centers , 2007, Bioinspiration & biomimetics.

[138]  Ali Maroosi,et al.  Application of honey-bee mating optimization algorithm on clustering , 2007, Appl. Math. Comput..

[139]  Alok Singh,et al.  An artificial bee colony algorithm for the leaf-constrained minimum spanning tree problem , 2009, Appl. Soft Comput..

[140]  M Reyes Sierra,et al.  Multi-Objective Particle Swarm Optimizers: A Survey of the State-of-the-Art , 2006 .

[141]  H. Abbass A single queen single worker honey–bees approach to 3-SAT , 2001 .

[142]  Sung Hoon Jung,et al.  Queen-bee evolution for genetic algorithms , 2003 .

[143]  Long Wang,et al.  The Crucial Problem of the NSS in the Ecommerce , 2007 .

[144]  Barry J. Adams,et al.  Honey-bee mating optimization (HBMO) algorithm for optimal reservoir operation , 2007, J. Frankl. Inst..

[145]  Govind Sharan Dangayach,et al.  Modelling Process and Supply Chain Scheduling Using Hybrid Meta-heuristics , 2008 .

[146]  Horst F. Wedde,et al.  A comprehensive review of nature inspired routing algorithms for fixed telecommunication networks , 2006, J. Syst. Archit..

[147]  Martin Lindauer,et al.  The "Language" and Orientation of the Honey Bee , 1956 .

[148]  M. Azeem,et al.  Modified queen bee evolution based genetic algorithm for tuning of scaling factors of fuzzy knowledge base controller , 2004, Proceedings of the IEEE INDICON 2004. First India Annual Conference, 2004..

[149]  Rolf Drechsler,et al.  Applications of Evolutionary Computing, EvoWorkshops 2008: EvoCOMNET, EvoFIN, EvoHOT, EvoIASP, EvoMUSART, EvoNUM, EvoSTOC, and EvoTransLog, Naples, Italy, March 26-28, 2008. Proceedings , 2008, EvoWorkshops.

[150]  Georgios Dounias,et al.  Nature Inspired Intelligence for the Constrained Portfolio Optimization Problem , 2008, SETN.

[151]  Viera Rozinajová,et al.  Exploring Social Behaviour of Honey Bees Searching on the Web , 2007, 2007 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Workshops.

[152]  Afshar Abas,et al.  Honey-Bee Mating Optimization (HBMO) Algorithm in Optimization Problems , 2008 .

[153]  Oskar Dressler,et al.  Künstliche Intelligenz? , 1986, FIFF Jahrestagung.

[154]  Michael N. Huhns,et al.  Multiagent-Based Fault Tolerance Management for Robustness , 2008 .

[155]  Taher Niknam,et al.  Application of honey-bee mating optimization on state estimation of a power distribution system including distributed generators , 2008 .

[156]  A. Aubert,et al.  Modulation of social interactions by immune stimulation in honey bee, Apis mellifera, workers , 2008, BMC Biology.

[157]  Sameh Otri,et al.  Data clustering using the bees algorithm , 2007 .

[158]  D.T. Pham,et al.  Optimising Neural Networks for Identification of Wood Defects Using the Bees Algorithm , 2006, 2006 4th IEEE International Conference on Industrial Informatics.

[159]  Xinling Shi,et al.  On the Analysis of Performance of the Improved Artificial-Bee-Colony Algorithm , 2008, 2008 Fourth International Conference on Natural Computation.

[160]  Alberto Prieto,et al.  Computational intelligence and bioinspired systems , 2007, Neurocomputing.

[161]  Dervis Karaboga,et al.  Artificial Bee Colony (ABC) Optimization Algorithm for Solving Constrained Optimization Problems , 2007, IFSA.

[162]  Marco Dorigo,et al.  Swarm intelligence: from natural to artificial systems , 1999 .