Ant Algorithm and its Applications for Solving Large Scale Optimization Problems on Parallel Computers Stefka Fidanova Institute for Information and Communication.

Ant Algorithm and its Applications for Solving Large Scale Optimization

Problems on Parallel Computers

Stefka FidanovaInstitute for Information and

Communication TechnologiesBulgarian Academy of Sciences

Hard Optimization Problems

• Vehicle routing problem

• Decision making problem

• Cutting stock problem

• Sensor layout problem

• GPS surveying problem

• Multidimensional surface global optimum

Metaheuristics

• A metaheuristics are methods for solving a very general class of computational problems by combining user-given black-box procedures in the hope of obtaining a more efficient or more robust procedure. The name combines the Greek prefix "meta" ("beyond", here in the sense of "higher level") and "heuristic" (from ευρισκειν, heuriskein, "to find").

• Metaheuristics are generally applied to problems for which there is no satisfactory problem-specific algorithm or heuristic; or when it is not practical to implement such a method. Most commonly used metaheuristics are targeted to combinatorial optimization problems, but of course can handle any problem that can be recast in that form, such as solving boolean equations

Metaheuristics Methods

• Genetic algorithm

• Simulated annealing

• Tabu search

• Ant colony optimization

• Particle swarm optimization

Ant Colony Optimization

Ant Colony OptimizationProcedure ACOBegin

initialize the pheromonewhile stopping criterion not satisfied do

position each ant on a starting noderepeat

for each ant dochose next node

end foruntil every ant has build a solutionupdate the pheromone

end whileend

Level of Parallelism

• Problem division to subproblems

• Several ant colonies communicates after every iteration

• Several ant colonies without communications

Problem Division to Subproblems

1

2

3

4

Ant Colonies with communications

Ant Colonies without communications

Examples

• Sensor layout problem

• Global optimum on multidimensional surface

Wireless Sensor Network

• Reconnaissance

• Surveillance

• Forest fire prevention

• Volcano eruption study

• Health data monitoring

• Civil engineering

WSN Layout Problem

• High Energy Communication Node

• Sensing Radius

• Communication Radius

• Fully Covered and Connected Area

• Minimal Number of Sensors

Sensor Layout Problem

• Ant colonies without communications

Global Optimum on Multidimensional Surface

Global Optimum on Multidimensional Surface

• Surface decomposition

• 4 ant colonies on one processor

• Several colonies without communications

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