A Study of Parallel Approaches in MOACOs for solving the Bicriteria TSP
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Transcript of A Study of Parallel Approaches in MOACOs for solving the Bicriteria TSP
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A Study of Parallel Approaches in MOACOs for Solving the
Bicriteria TSPA.M. Mora, J.J. Merelo, P.A. Castillo,
M.G. Arenas, P. García-Sánchez, J.L.J. LaredoDepartamento de Arquitectura y Tecnología de Computadores.
UNIVERSIDAD DE GRANADA
International Work-Conference on Artificial Neural Networks
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INDEX
Ant Colony Optimization
Multi-Objective Problems
Bicriteria TSP
Parallelization
Experiments and Results
Conclusions and Future Work
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Metaheuristic inspired in the behaviour of ants when search for food
They ‘build’ (after some time) the shortest paths between the nest and the food sources
They communicate using the environment Depositing and following pheromone
Ant Colony Optimization (I)BIO-INSPIRATION
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Pheromone trail
?
Ant Colony Optimization (II)CHOOSING A PATH
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Ant Colony Optimization (III)MAIN FEATURES
A set of independent artificial agents They move in graphs searching for solutions They use a pheromone matrix to decide
where to move In addition, they consider heuristic functions
to build the solutions
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Multi-Objective ProblemsDEFINITION
There are some functions to optimise One solution must satisfy some criteria Dominance concept:
one solution dominates another one, if it has a better cost in one of the objectives and at least the same cost in the others
There is a set of solutions, the Pareto Set
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Bicriteria TSPTHE PROBLEM
Search for the Hamiltonian circuit which minimizes the cost of the edges to go through (distance)
There is a MO-TSP, which has associated some costs to each edge
The Bicriteria TSP considered two costs
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Parallelization (I)AIM
Two profits:• Yield better results• Improve the running time
Two Schemes (at colony level):• Space specialized
colonies• Objective specialized
colonies
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Parallelization (II)APPROACHES
BIANT (by Iredi et al.)• It is an Ant System• Two pheromone matrices• Two heuristic functions• parameter to weight the objectives
MOACS (by Gambardella and Barán et al.)• It is an Ant Colony System• One pheromone matrix• Two heuristic and costs functions• parameter to weight the objectives
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Bi-criteria KROA-100 Problem. 16 processors
Experiments and Results (I)BIANT
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Bi-criteria KROA-100 Problem. 16 processors
Experiments and Results (II)MOACS
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Bi-criteria KROA-100 Problem. 11 processors
Experiments and Results (III)MOACS (Pareto Set comparisons)
Solutions in each one of the processors
Global Pareto Set
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Bi-criteria KROA-100 Problem. 16 processors
Experiments and Results (IV)Non-dominated solutions
Number of non-dominated solutions in the
global Pareto Set
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Bi-criteria KROA-100 Problem. 16 processors
Experiments and Results (V)Running Time
Scalability graph
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Two well-known MOACOs have been implemented in a parallel shape. Two different searching schemes have been applied (SSC and OSC). Improving in the sets of solutions. Improving in the running time.
Implement a heterogeneous scheme Test other instances and problems Implement an ant-level parallelization Use a higher number of processors
Conclusions and Future Work
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Thank You!
The End