Honey Bee Algorithm

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4.1 Introduction This chapter provides a comprehensible introduction to the relevant work done in finding multiple paths in static as well as in dynamic environment. Considerable amount of work has been done for finding multiple paths in static environment, while much research is needed in the field of dynamic and noisy environments. Different variations of ABC algorithm in environment can be roughly categorized in following strategies:  Clustering techniques in ABC  Wireless network simulation  Path planning  Generalized assignment problems  Hybrid Methods (incorporating other evolutionary algorithms with ABC)  Neural networks  Data mining  Civil engineering etc. Dervis Karaboga and Bahriye Basturk [14] extend the Artificial…show more content…
ABC simulates the intelligent foraging behavior of a honeybee swarm .In it defines the different forms of honey bee artificial algorithm on different situations to find the best optimal solution also describe the working of honey bee algorithm as it contains two components its population and fitness ABC generates a randomly distributed initial population PC of SN solutions (food source positions), where SN denotes the size of employed bees or onlooker bees. Each solution is a D-dimensional vector.it follows a cycle which consist of three steps sending the employed bees onto their food sources and evaluating their nectar amounts; after sharing the nectar information of food sources, the selection of food source regions by the onlookers and evaluating the nectar amount of the food sources; determining the scout bees and then sending them randomly onto possible new food sources. We repeat the steps through predetermined number of until a termination criterion is…show more content…
2011 P-DABC flexible job shop scheduling uses a crossover operator in the employed phase and external Pareto archive set [60] 5. 2010 GABC benchmark functions integrates global best information (gbest) into the solution search equation [61] 6. 2011 CABC Traveling Salesman integrates mutation operator in the employed and onlooker phase [62] 7. 2009 ABC quadratic knapsack uses heuristic to fix infeasible solution and local search for enhancement of exploration [63] 8. 2010 ABC quadratic minimum spanning tree uses tabu search to determine the new neighbouring food source [64] 9. 2009 HJABC benchmark functions integrates Hooke Jeeves and rank based fitness to enhance ABC [65] 10. 2011 RABC complex benchmark instances uses rotational direction method for the exploitation [66] 11. 2011 DABC flow shop scheduling uses self adaptive strategy to generate a neighboring solution [67] 12. 2009 discrete ABC data clustering uses GRASP for the solution of the clustering [68] 13. 2009 HSABC structural inverse analysis hybridizes ABC with Nelder-Mead simplex [69] 14. 2011 DABC Flowshop Scheduling hybridizes iterated greedy algorithm with a local search into the ABC components

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