ant colony optimization with multiple objectives hong zhou computer systems lab 2009-2010 quarter 3...
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Ant Colony Optimization with Multiple Objectives
Hong ZhouComputer Systems Lab 2009-2010
Quarter 3Period 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
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.
Possible Application• Military path finding: Speed versus Safety
• Airplane routing
Left is the faster route, right the safer. Lighter means higher elevation, darker lower
Development
• Java• ACO, single objective• Improve/modify
Elitist ant variation (affects the pheromone update)
• Multiple objective, two variables
• Output file
To Be Implemented
• Two objectives– Two pheromones– Two ‘distances’
• Weighting objectives• Check against single
objective
• Display– Is there a way to show
two weights distinctly?
• How to calculate an overall ‘score’ or ‘length’– Is there a need to?
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
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;}
• Ant deposit:
edg.deposit(ws * QCONST/getTourLength());
• Updated in Edge:
tau = RHO * getTau() + deltaTau; deltaTau = 0.0;
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
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
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