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Ant Colony Optimization with Multiple Objectives

Hong Zhou Computer Systems Lab 2009-2010 Quarter 4 Period 2. Ant Colony Optimization with Multiple Objectives. Ant Colony Optimization. Based on real ants Useful method to find near optimal paths. Ants choose their individual paths based on pheromones left by other ants.

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Ant Colony Optimization with Multiple Objectives

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  1. Hong Zhou Computer Systems Lab 2009-2010 Quarter 4Period 2 Ant Colony Optimization with Multiple Objectives

  2. Ant Colony Optimization • Based on real ants • 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. Program Structure • ACO  Network  Nodes, Edges, Ants • Node: has an ID and a weight, has ants ‘on’ it • Edge: has two ‘length’, where pheromone is stored • Ant: chooses next node, keeps track of path

  6. Setup • 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

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

  8. Multiple Objectives • Two objectives • Two pheromones • Two ‘distances’ • Display • Weighting objectives • Calculating overall length

  9. As Program Runs

  10. Different variables Objective weight = 0 Objective weight = 1

  11. Results, Conclusions Objective weight = 1 Objective weight = 0 Weight 1 average = 3382 Weight 2 average = 3659 Weight 1 average = 2141 Weight 2 average = 2934

  12. Works Cited • [1] Blum, C. Ant colony optimization: Introduction and recent trends. • Barcelona, Spain 2005 • [2] Garcia-Martinez, C., Cordon, O., Herrera, F. A taxonomy and an empirical analysis of multiple objective ant colony optimization algorithms for the bi-criteria TSP. Granada, Spain 2006 • [3] McMullen, P. R. An ant colony optimization approach to addressing a JIT sequencing problem with multiple objectives. Auburn, USA 2001 • [4] Mora, A.M. Balancing Safety and Speed in the Military Path Finding • Problem: Analysis of Different ACO Algorithms. Granada, Spain 2007 • [5] 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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