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Ant Colony Optimization with Multiple Objectives

Hong Zhou Computer Systems Lab 2009-2010 Quarter 3 Period 2. Ant Colony Optimization with Multiple Objectives. Ant Colony Optimization. Based on how real ants cooperate to find food Useful method to find near optimal paths.

colin-leonard
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Ant Colony Optimization with Multiple Objectives

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  1. Hong Zhou Computer Systems Lab 2009-2010 Quarter 3Period 2 Ant Colony Optimization with Multiple Objectives

  2. Ant Colony Optimization • Based on how real ants cooperate to find food • Useful method to find near optimal paths. • Ants choose their individual paths based on pheromones left by other ants. • They end up all going on the near optimal path

  3. Multiple Objectives • Many variables effect how good a path is in real life • Could be weighted differently (currently 50% each in mine) • Results in different solutions (often less optimal in one variable but better overall) • Focus of the research: weight that would give a better path overall + improving system as a whole.

  4. Possible Application • Military path finding: Speed versus Safety • Airplane routing Left is the faster route, right the safer. Lighter means higher elevation, darker lower

  5. Development • Java • ACO, single objective • Improve/modify Elitist ant variation (affects the pheromone update) • Multiple objective, two variables • Output file

  6. Display Is there a way to show two weights distinctly? How to calculate an overall ‘score’ or ‘length’ Is there a need to? To Be Implemented • Two objectives • Two pheromones • Two ‘distances’ • Weighting objectives • Check against single objective

  7. Current Code • ACO  Network  Nodes, Edges, Ants • Node: has an ID and a weight, has ants ‘on’ it • Edge: has a ‘length’, where pheromone is stored • Ant: chooses next node, keeps track of path

  8. Ant deposit: edg.deposit(ws * QCONST/getTourLength()); Updated in Edge: tau = RHO * getTau() + deltaTau; deltaTau = 0.0; Important Bits of Code • Ant: picking next node double rn = rnd.nextDouble();sumPij = 0.0;for (i=0; i < nan; i++) { sumPij += pij[i]; if (rn <= sumPij) break;}

  9. Results • Current set up: • Network: • Node weights reflected by color • Edges with length • Starts out with no ants • Output: • Displays #steps, best tour length • Prints out each ant’s result as it finishes

  10. As Program Runs

  11. Results, Conclusions • Best ever tour length versus path the ants congregate to: • 1182.5 versus approximately 1300 • Average tour length to be implemented • Outputting to a file • Determining actual best possible tour • Multiple Objective: • 1252.5 and about 1550, as expected slower

  12. Works Cited • [1] Blum, C. Ant colony optimization: Introduction and recent trends. • Barcelona, Spain 2005 • [2] Mora, A.M. Balancing Safety and Speed in the Military Path Finding • Problem: Analysis of Different ACO Algorithms. Granada, Spain 2007 • [3] Mora, A.M. CHAC. A MOACO Algorithm for Computation of Bi- • Criteria Military Unit Path in the Battlefield. Granada, Spain 2006

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