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Computational Intelligence-Topic 3: Ant Colony Optimization 李长河 Changhe.lw@gmail.com

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ppt slide no 2 content not found

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Outline Introduction (Swarm intelligence) Natural behavior of ants First Algorithm: Ant System Improvements to Ant System Applications

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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

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http://www.scs.carleton.ca/~arpwhite/courses/95590Y/notes/SI%20Lecture%203.pdf

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http://www.scs.carleton.ca/~arpwhite/courses/95590Y/notes/SI%20Lecture%203.pdf

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Inherent features Inherent parallelism Stochastic nature Adaptivity Use of positive feedback Autocatalytic in nature

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Natural behavior of an ant Foraging modes Wander mode Search mode Return mode Attracted mode Trace mode Carry mode

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Natural behavior of ant Ant Algorithms – (P.Koumoutsakos – based on notes L. Gamberdella (www.idsia.ch)

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How to implement in a program Ants: Simple computer agents Move ant: Pick next component in the construction of solution Trace: Pheromone, , a global type of information Memory: MK or TabuK Next move: Use probability to move ant

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ACO for the Traveling Salesman Problem The TSP is a very important problem in the context of Ant Colony Optimization because it is the problem to which the original AS was first applied, and it has later often been used as a benchmark to test a new idea and algorithmic variants. It is a metaphor problem for the ant colony It is one of the most studied NP-hard problems in the combinatorial optimization it is very easily to explain. So that the algorithm behavior is not obscured by too many technicalities.

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Ant Colony Optimization (ACO) for TSP Graph (N,E): where N = cities(nodes), E = edges = the tour cost from city i to city j (edge weight) Ant move from one city i to the next j with some transition probability.

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Each edge is associated a static value based on the edge-cost (r,s) = 1/dr,s. Each edge of the graph is augmented with a trace (r,s) deposited by ants. Initially, 0. Trace is dynamic and it is learned at run-time Each ant tries to produce a complete tour, using the probability depending on (r,s) and (r,s) to choose the next city. Ant Colony Optimization (ACO) for TSP

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ACO Algorithm for TSP Initialize Place each ant in a randomly chosen city Choose NextCity(For Each Ant) more cities to visit For Each Ant Return to the initial cities Update trace level using the tour cost for each ant Print Best tour yes No Stopping criteria yes No

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A simple TSP example A E D C B dAB =100;dBC = 60…;dDE =150

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Iteration 1 A E D C B

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How to choose next city? A E D C B

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Iteration 2 A E D C B

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Iteration 3 A E D C B

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Iteration 4 A E D C B

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Iteration 5 A E D C B

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Path and Trace Update L1 =300 L2 =450 L3 =260 L4 =280 L5 =420

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End of First Run All ants die New ants are born Save Best Tour (Sequence and length)

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Ant System (Ant Cycle) Dorigo [1] 1991 t = 0; NC = 0; τij(t)=c for ∆τij=0 Place the m ants on the n nodes Update tabuk(s) Compute the length Lk of every ant Update the shortest tour found = For every edge (i,j) Compute For k:=1 to m do Initialize Choose the city j to move to. Use probability Tabu list management Move k-th ant to town j. Insert town j in tabuk(s) Set t = t + n; NC=NC+1; ∆τij=0 NC

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Stopping Criteria Stagnation Max Iterations

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ACO : Ant Colony Optimization for TSP

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• Algorithm found best solutions on small problems (75 city) • On larger problems converged to good solutions – but not the best • On “static” problems like TSP hard to beat specialist algorithms • Ants are “dynamic” optimizers – should we even expect good performance on static problems • Coupling ant with local optimizers gave world class results…. Performance

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Parameters of ACO Taken from Dorigo [1] Comparison among three strategies, averages over 10 trials. Other parameters: Q, constant for trace updates, and m, the number of ants

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Pheromone trail and heuristic function: are they useful? Comparison between ACS standard, ACS with no heuristic (i.e., we set B=0), and ACS in which ants neither sense nor deposit pheromone. Problem: Oliver30. Averaged over 30 trials, 10,000/m iterations per trial.

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General ACO A stochastic construction procedure Probabilistically build a solution Iteratively adding solution components to partial solutions - Heuristic information - Trace/Pheromone trail Reinforcement Learning reminiscence Modify the problem representation at each iteration

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General ACO Ants work concurrently and independently Collective interaction via indirect communication leads to good solutions

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Some inherent advantages Positive Feedback accounts for rapid discovery of good solutions Distributed computation avoids premature convergence The greedy heuristic helps find acceptable solution in the early solution in the early stages of the search process. The collective interaction of a population of agents.

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Disadvantages in Ant Systems Slower convergence than other Heuristics Performed poorly for TSP problems larger than 75 cities. No centralized processor to guide the AS towards good solutions

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Applications Traveling Salesman Problem Quadratic Assignment Problem Network Model Problem Vehicle routing

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Conclusion ACO is a relatively new metaheuristic approach for solving hard combinatorial optimization problems. Artificial ants implement a randomized construction heuristic which makes probabilistic decisions. The cumulated search experience is taken into account by the adaptation of the pheromone trail. ACO shows great performance with the “ill-structured” problems like network routing. In ACO local search is important to obtain good results.

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References Dorigo M. and G. Di Caro (1999). The Ant Colony Optimization Meta-Heuristic. In D. Corne, M. Dorigo and F. Glover, editors, New Ideas in Optimization, McGraw-Hill, 11-32. M. Dorigo and L. M. Gambardella. Ant colonies for the traveling salesman problem. BioSystems, 43:73–81, 1997. M. Dorigo and L. M. Gambardella. Ant Colony System: A cooperative learning approach to the traveling salesman problem. IEEE Transactions on Evolutionary Computation, 1(1):53–66, 1997. G. Di Caro and M. Dorigo. Mobile agents for adaptive routing. In H. El-Rewini, editor, Proceedings of the 31st International Conference on System Sciences (HICSS-31), pages 74–83. IEEE Computer Society Press, Los Alamitos, CA, 1998. M. Dorigo, V. Maniezzo, and A. Colorni. The Ant System: An autocatalytic optimizing process. Technical Report 91-016 Revised, Dipartimento di Elettronica,Politecnico di Milano, Italy, 1991. L. M. Gambardella, ` E. D. Taillard, and G. Agazzi. MACS-VRPTW: A multiple ant colony system for vehicle routing problems with time windows. In D. Corne, M. Dorigo, and F. Glover, editors, New Ideas in Optimization, pages 63–76. McGraw Hill, London, UK, 1999. L. M. Gambardella, ` E. D. Taillard, and M. Dorigo. Ant colonies for the quadratic assignment problem. Journal of the Operational Research Society,50(2):167–176, 1999. V. Maniezzo and A. Colorni. The Ant System applied to the quadratic assignment problem. IEEE Transactions on Data and Knowledge Engineering, 11(5):769–778, 1999. Gambardella L. M., E. Taillard and M. Dorigo (1999). Ant Colonies for the Quadratic Assignment Problem. Journal of the Operational Research Society, 50:167-176.

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