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IE 607 Heuristic Optimization Ant Colony Optimization

IE 607 Heuristic Optimization Ant Colony Optimization. Double Bridge Experiment. Behavior of Real Ants. Real Ants Find the Shortest Path to Food Resource Pheromone Is Laid by Ants along the Trail Pheromone Evaporates over Time

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IE 607 Heuristic Optimization Ant Colony Optimization

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  1. IE 607 Heuristic OptimizationAnt Colony Optimization

  2. Double Bridge Experiment

  3. Behavior of Real Ants • Real Ants Find the Shortest Path to Food Resource • Pheromone Is Laid by Ants along the Trail • Pheromone Evaporates over Time • Pheromone Intensity Increases with Number of Ants Using Trail • Good Paths Are Reinforced And Bad Paths Gradually Disappear

  4. ACO • Meta-Heuristic Optimization Method • Inspired by Real Ants • First published by Marco Dorigo (1992) as his dissertation • Is currently greatly expanding in applications and interest, mainly centered in Europe • Positive & Negative Feedback • Constructive Greedy Heuristic • Population-based Method

  5. Application • TSP • QAP • VRP • Telecommunication Network • Scheduling • Graph Coloring • Water Distribution Network • etc

  6. Methodology ACO ACO Algorithm Set all parameters and initialize the pheromone trails Loop Sub-Loop Construct solutions based on the state transition rule Apply the online pheromone update rule Continue until all ants have been generated Apply Local Search Evaluate all solutions and record the best solution so far Apply the offline pheromone update rule Continue until the stopping criterion is reached

  7. Methodology Overview of ACO Algorithm • Each ant represents a complete solution • Online updating is performed each time after an ant constructed a solution: more chance to exploration • Local search is applied after all ants construct solutions • Offline updating is employed after the local search: allow good ants to contribute

  8. Methodology Parameters of ACO Algorithm : Pheromone trail of combination (i,j) : Local heuristic of combination (i,j) : Transition probability of combination (i,j) : Relative importance of pheromone trail : Relative importance of local heuristic : Determines the relative importance of exploitation versus exploration : Trail persistence

  9. Methodology Ant System (AS) – the earliest version of ACO State Transition Probability Pheromone Update Rule

  10. Methodology ASelite ASrank

  11. Methodology Ant-Q & Ant Colony System (ACS) Local Updating (Online Updating) Global Updating (Offline Updating) Exploitation Exploration

  12. Methodology Max-Min Ant System (MMAS) ANTS

  13. Website & Books • http://iridia.ulb.ac.be/~mdorigo/ACO/ACO.html • Bonabeau E., M. Dorigo & T. Theraulaz (1999). From Natural to Artificial Swarm Intelligence. New York: Oxford University Press. • Corne D., M. Dorigo & F. Glover, Editors (1999). New Ideas in Optimisation. McGraw-Hill .

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