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Published on : Jan 08, 2015
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Ant Colony Optimization Prepared by: Ahmad Elshamli, Daniel Asmar, Fadi Elmasri

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Ant Colony Optimization Prepared by: Ahmad Elshamli, Daniel Asmar, Fadi Elmasri Presentation Outline Section I (Introduction) Historical Background Ant System Modified algorithms Section II (Applications) TSP QAP Section III (Applications +Conclusions) NRP VRP Conclusions, limitations and Danny Fadi Ahmad Section 1 Introduction (Swarm intelligence) Natural behavior of ants First Algorithm: Ant System Improvements to Ant System Applications Swarm intelligence Collective system capable of accomplishing difficult tasks in dynamic and varied environments without any external guidance or control and with no central coordination Achieving a collective performance which could not normally be achieved by an individual acting alone Constituting a natural model particularly suited to distributed problem solving http://www.scs.carleton.ca/~arpwhite/courses/95590Y/notes/SI%20Lecture%203.pdf http://www.scs.carleton.ca/~arpwhite/courses/95590Y/notes/SI%20Lecture%203.pdf http://www.scs.carleton.ca/~arpwhite/courses/95590Y/notes/SI%20Lecture%203.pdf http://www.scs.carleton.ca/~arpwhite/courses/95590Y/notes/SI%20Lecture%203.pdf http://www.scs.carleton.ca/~arpwhite/courses/95590Y/notes/SI%20Lecture%203.pdf http://www.scs.carleton.ca/~arpwhite/courses/95590Y/notes/SI%20Lecture%203.pdf Inherent features Inherent parallelism Stochastic nature Adaptivity Use of positive feedback Autocatalytic in nature Natural behavior of an ant Foraging modes Wander mode Search mode Return mode Attracted mode Trace mode Carry mode Natural behavior of ant Ant Algorithms – (P.Koumoutsakos – based on notes L. Gamberdella (www.idsia.ch) Work to date Work to date How to implement in a program Ants: Simple computer agents Move ant: Pick next component in the const. solution Pheromone: Memory: MK or TabuK Next move: Use probability to move ant

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