Enhancement of Bee Colony Algorithm Using 2-Opt Technique for Constructing Optimal Path

Topics: Bees, Honey bee, Beekeeping Pages: 5 (1265 words) Published: April 2, 2013
Enhancement of Bee colony algorithm using 2-opt technique for constructing optimal path

Bindia Sonika Jaspreet Kaur Sahiwal Dept. of CSE, Lovely Professional University Dept. of CSE, Lovely Professional University Dept. of CSE, Lovely Professional University Phagwara, India Phagwara, India Phagwara, India

kaushal_ne22@yahoo.co.in Personality.sonika@gmail.com jaspreet.14752@lpu.co.in

Abstract- The Bees Algorithm is an optimization algorithm to find the optimal path solution. Bee Colony Optimization Algorithm depicts the natural behavior of real honey bees in food foraging. Honey bees use several mechanisms like waggle dance to optimally locate food sources and to search new ones. This makes them a good candidate for developing new intelligent search algorithms. The BCO model is used to generate a set of feasible solutions rather than using a pseudorandom approach. Our proposed 2-opt algorithm basically removes two edges from the tour, and reconnects the two paths created. This is very much better from existing method. Some advantages of applying 2-opt are the simplicity in its implementation and its ability to obtain near optimal results. The basic idea is to eliminate two arcs in R in order to obtain two different paths.

Index terms- Swarm intelligence, waggle dance, Bee colony, , Data access, Multi-agent System, Query cycling process I. Introduction
Swarm Intelligence is a design framework based on social insect behaviour such as ants, bees, and wasps are unique in the way these simple individuals cooperate to accomplish complex, difficult tasks.Properties in swarm intelligent systems include: Robustness against individual misbehaviour or loss, Flexibility to change quickly in a dynamic environment. Swarm behavior can be seen in bird flocks, fish schools, as well as in insects like mosquitoes. The main principles of the collective behavior are: Homogeneity: every bird in flock has the same behavior model. The flock moves without a leader, even though temporary leaders seem to appear, Locality: the motion of each bird is only influenced by its nearest flock mates, Collision Avoidance: avoid collision with nearby flock mates, Velocity Matching: attempt to match velocity with nearby flock mates, Flock Centering: attempt to stay close to nearby flock mates BCO depicts the natural behavior of real honey bees in food foraging. Honey bees use several mechanisms like waggle dance to optimally locate food sources and to search new ones. This makes them a good candidate for developing new intelligent search algorithms.Honey bees also exhibit swarm intelligence. They use an odour for conveying information. Waggle dance is a term used   for a particular figure-eight dance of the honey bees. By performing this dance, successful foragers can share with their hive mate’s information about the direction and distance to patches of flowers yielding nectar and pollen, to water sources, or to new housing locations. Methods of decision making is done by following analysis: Colony-level analysis: selective exploitation of nectar sourcesIndividual-level analysis: assessing nectar source profitability. The proposed 2-opt algorithm basically removes two edges from the tour, and reconnects the two paths created. This is often referred to as a 2-opt move. There is only one way to reconnect the two paths so that we still have a valid tour. We do this only if the new tour will be shorter. Continue removing and reconnecting the tour until no 2-opt improvements can be found. The tour is now 2-optimal. II. PROPOSED ALGORITHIM

Here we will first see the existing BCO algorithm that is as follows.

BCO...

References: [1] Saif Mahmood Saab, Dr. Nidhal Kamel Taha El-Omari, Dr. Hussein H. Owaied “Devoloping optimization algorithm using artificial bee colony system” ”, UbiCC Journal-Vol 4, No 5, 2009, pp 391-396 .
[2] Li-Pei Wong, Malcolm Yoke Hean Low “A Bee Colony Optimization Algorithm for Traveling Salesman Problem ”, Chin Soon Chong 6th IEEE Int. Conf. on Industrial Informatics 2008, pp 1019-1025
[3] Xiaojun Bi, Yanjiao “An Improved Artificial Bee Colony Algorithm”, Computer Research & Development (ICCRD), 2011, 3rd Int. Conf. on 11-13 March,2011 pp 174-177
[4] Dr. Arvind Kaur, Shivangi Goyal “A Survey on the Applications of Bee Colony Optimization Techniques”,  Guru Gobind Singh Indraprastha University, Dwarka , 2011.
[5] www.enggjournals.com/ijcse/doc/IJCSE [5] Hugh J. Watson
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[6] Dusan Teodoravic Mauro “Bee colony optimization- A cooperative learning approach to complex transportation problems”, ACM Transactions on Computational Logic 2011, proceedings of 16th Mini-Euro Conf. on Advanced OR and AI methods in transportation, pp51-60
[7] Shivangi Goyal, “A Bee Colony Optimization Algorithm for Fault Coverage Based Regression Test Suite Prioritization” (2011).
[8] Christian Nilsson, Linkoping University, “Heuristics for the Traveling Salesman Problem” Tech report, Sweden, 2003
[9] Simon Garnier, Jacques Guatrais, Guy Theraulaz, “The Biological Principles of Swarm Intelligence” 2007, © Springer Science+ Business Media, pp 3-31
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