Bees algorithm

The Bees Algorithm is a population-based search algorithm first developed in 2005. [Pham DT, Ghanbarzadeh A, Koc E, Otri S, Rahim S and Zaidi M. The Bees Algorithm. Technical Note, Manufacturing Engineering Centre, Cardiff University, UK, 2005] It mimics the food foraging behaviour of swarms of honey bees. In its basic version, the algorithm performs a kind of neighbourhood search combined with random search and can be used for both combinatorial optimisationDuc Truong Pham, Ashraf Afify, Ebubekir Koc " [http://conference.iproms.org/manufacturing_cell_formation_using_the_bees_algorithm_0 Manufacturing cell formation using the Bees Algorithm] ". IPROMS 2007 Innovative Production Machines and Systems Virtual Conference, Cardiff, UK.] D. T. Pham, E. Koç, J. Y. Lee, and J. Phrueksanant, Using the Bees Algorithm to schedule jobs for a machine, Proc Eighth International Conference on Laser Metrology, CMM and Machine Tool Performance, LAMDAMAP, Euspen, UK, Cardiff, p. 430-439, 2007.] and functional optimisation.Pham D.T., Ghanbarzadeh A., Koç E., Otri S., Rahim S., and M.Zaidi " [http://conference.iproms.org/the_bees_algorithm_a_novel_tool_for_complex_optimisation_problems The Bees Algorithm – A Novel Tool for Complex Optimisation Problems] "", Proceedings of [http://conference.iproms.org/iproms_2006_0 IPROMS 2006 Conference] , pp.454-461]

The foraging process in nature

A colony of honey bees can extend itself over long distances (up to 14 km) and in multiple directions simultaneously to exploit a large number of food sources.A colony prospers by deploying its foragers to good fields. In principle, flower patches with plentiful amounts of nectar or pollen that can be collected with less effort should be visited by more bees, whereas patches with less nectar or pollen should receive fewer bees. [Von Frisch K. Bees: Their Vision, Chemical Senses and Language. (Revised edn) Cornell University Press, N.Y., Ithaca, 1976.] [Seeley TD. The Wisdom of the Hive: The Social Physiology of Honey Bee Colonies. Massachusetts: Harvard University Press, Cambridge, 1996.] [Bonabeau E, Dorigo M, and Theraulaz G. Swarm Intelligence: from Natural to Artificial Systems. Oxford University Press, New York, 1999.] [Camazine S, Deneubourg J, Franks NR, Sneyd J, Theraula G and Bonabeau E. Self-Organization in Biological Systems. Princeton: Princeton University Press, 2003.]

The foraging process begins in a colony by scout bees being sent to search for promising flower patches. Scout bees move randomly from one patch to another. During the harvesting season, a colony continues its exploration, keeping a percentage of the population as scout bees.

When they return to the hive, those scout bees that found a patch which is rated above a certain quality threshold (measured as a combination of some constituents, such as sugar content) deposit their nectar or pollen and go to the “dance floor” to perform a dance known as the waggle dance. [Von Frisch K. Bees: Their Vision, Chemical Senses and Language. (Revised edn) Cornell University Press, N.Y., Ithaca, 1976.]

This dance is essential for colony communication, and contains three pieces of information regarding a flower patch: the direction in which it will be found, its distance from the hive and its quality rating (or fitness). This information helps the colony to send its bees to flower patches precisely, without using guides or maps. Each individual’s knowledge of the outside environment is gleaned solely from the waggle dance. This dance enables the colony to evaluate the relative merit of different patches according to both the quality of the food they provide and the amount of energy needed to harvest it. After waggle dancing on the dance floor, the dancer (i.e. the scout bee) goes back to the flower patch with follower bees that were waiting inside the hive. More follower bees are sent to more promising patches. This allows the colony to gather food quickly and efficiently.

While harvesting from a patch, the bees monitor its food level. This is necessary to decide upon the next waggle dance when they return to the hive. If the patch is still good enough as a food source, then it will be advertised in the waggle dance and more bees will be recruited to that source.

Proposed Bees Algorithm

The Bees Algorithm is an optimisation algorithm inspired by the natural foraging behaviour of honey bees to find the optimal solution. The algorithm requires a number of parameters to be set, namely: number of scout bees (n), number of sites selected out of n visited sites (m), number of best sites out of m selected sites (e), number of bees recruited for best e sites (nep), number of bees recruited for the other (m-e) selected sites (nsp), initial size of patches (ngh) which includes site and its neighbourhood and stopping criterion.

The pseudo code for the bees algorithm in its simplest form: 1. Initialise population with random solutions. 2. Evaluate fitness of the population. 3. While (stopping criterion not met) //Forming new population. 4. Select sites for neighbourhood search. 5. Recruit bees for selected sites (more bees for best e sites) and evaluate fitnesses. 6. Select the fittest bee from each patch. 7. Assign remaining bees to search randomly and evaluate their fitnesses. 8. End While.

In first step, the bees algorithm starts with the scout bees (n) being placed randomly in the search space. In step 2, the fitnesses of the sites visited by the scout bees are evaluated.In step 4, bees that have the highest fitnesses arechosen as “selected bees” and sites visited by themare chosen for neighbourhood search. Then, in steps5 and 6, the algorithm conducts searches in theneighbourhood of the selected sites, assigning morebees to search near to the best e sites. The bees canbe chosen directly according to the fitnessesassociated with the sites they are visiting.Alternatively, the fitness values are used todetermine the probability of the bees being selected.Searches in the neighbourhood of the best e siteswhich represent more promising solutions are mademore detailed by recruiting more bees to follow themthan the other selected bees. Together with scouting,this differential recruitment is a key operation of theBees Algorithm. However, in step 6, for each patch only the beewith the highest fitness will be selected to form thenext bee population. In nature, there is no such arestriction. This restriction is introduced here toreduce the number of points to be explored. In step 7,the remaining bees in the population are assignedrandomly around the search space scouting for newpotential solutions. These steps are repeated until astopping criterion is met. At the end of each iteration,the colony will have two parts to its new population - those that were the fittest representatives from a patch and those that have been sent out randomly.

Applications

The Bees Algorithm, which is inspired by the food foraging behaviour of honey bees, has found many applications in engineering field, such as:
*Training neural networks for pattern recognition. [D. T. Pham, E. Koç, A. Ghanbarzadeh, and S. Otri, Optimisation of the weights of multi-layered perceptrons using the Bees Algorithm, Proc 5th International Symposium on Intelligent Manufacturing Systems, Turkey, 2006.] [D. T. Pham, A. Ghanbarzadeh, E. Koç, and S. Otri, Application of the Bees Algorithm to the training of radial basis function networks for control chart pattern recognition, Proc 5th CIRP International Seminar on Intelligent Computation in Manufacturing Engineering (CIRP ICME '06), Ischia, Italy, 2006.] [D. T. Pham, S. Otri, A. Ghanbarzadeh, and E. Koç, Application of the Bees Algorithm to the training of learning vector quantisation networks for control chart pattern recognition, Proc Information and Communication Technologies (ICTTA'06), Syria, p. 1624-1629, 2006.] [D. T. Pham, A. J. Soroka, A. Ghanbarzadeh, E. Koç, S. Otri, and M. Packianather, Optimising neural networks for identification of wood defects using the Bees Algorithm, Proc 2006 IEEE International Conference on Industrial Informatics, Singapore, 2006.] [Pham D. T., Zaidi Muhamad, Massudi Mahmuddin, Afshin ghanbarzadeh, Ebubekir Koc, Sameh Otri. Using the bees algorithm to optimise a support vector machine for wood defect classification. IPROMS 2007 Innovative Production Machines and Systems Virtual Conference, Cardiff, UK." [http://conference.iproms.org/using_the_bees_algorithm_to_optimise_a_support_vector_machine_for_wood_defect_classification] " ]
*Forming manufacturing cells.
*Scheduling jobs for a production machine.
*Finding multiple feasible solutions to a preliminary design problems. [D. T. Pham, M. Castellani, and A. Ghanbarzadeh, Preliminary design using the Bees Algorithm, Proc Eighth International Conference on Laser Metrology, CMM and Machine Tool Performance, LAMDAMAP, Euspen, UK, Cardiff, p. 420-429, 2007.]
*Data clustering [D. T. Pham, S. Otri, A. A. Afify, M. Mahmuddin, and H. Al-Jabbouli, Data clustering using the Bees Algorithm, Proc 40th CIRP Int. Manufacturing Systems Seminar, Liverpool, 2007.]
*Optimising the design of mechanical components. [D. T. Pham, A. J. Soroka, E. Koç, A. Ghanbarzadeh, and S. Otri, Some applications of the Bees Algorithm in engineering design and manufacture, Proc Int. Conference on Manufacturing Automation (ICMA 2007), Singapore, 2007.]
*Multi-Objective Optimisation. [Pham D.T., Ghanbarzadeh A. " [http://conference.iproms.org/multi_objective_optimisation_using_the_bees_algorithm Multi-Objective Optimisation using the Bees Algorithm] "", Proceedings of [http://conference.iproms.org IPROMS 2007 Conference] ]
*Tuning a fuzzy logic controller for a robot gymnast. [D.T Pham, Ahmed Haj Darwish, E.E Eldukhri, Sameh Otri. " [http://conference.iproms.org/using_the_bees_algorithm_to_tune_a_fuzzy_logic_controller_for_a_robot_gymnast_0 Using the Bees Algorithm to tune a fuzzy logic controller for a robot gymnast.] "", Proceedings of [http://conference.iproms.org IPROMS 2007 Conference] ]

ee also

*Evolutionary computation
*Swarm intelligence
*Manufacturing Engineering Centre

References

External links

* [http://www.bees-algorithm.org The Bees Algorithm Official Website]
* [http://www.cf.ac.uk Cardiff University]
* [http://www.mec.cf.ac.uk Manufacturing Engineering Centre]
* [http://www.mec.cf.ac.uk/research/body_pics/Bees_Poster.ppt The Bees Algorithm - First Prize-winning Poster]
* [http://www.mec.cf.ac.uk/research/pubs/BEES%20on%20BBC%20Wales.mp3 BBC Interview Records]
* [http://www.mec.cf.ac.uk/research/body_pics/Bees_Poster.ppt MEC Bees won ‘best communication’ prize at INTEGR8OR]
* [http://news.bbc.co.uk/1/hi/wales/south_east/5285106.stm Boffins put dancing bees to work - BBC News]