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A Study of Parallel Approaches in MOACOs for solving the Bicriteria TSP

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Antonio Mora
Antonio Mora

In this work, the parallelization of some Multi-Objective Ant Colony Optimization (MOACO) algorithms has been performed. The aim is to get a better performance, not only in running time (usually the main objective when a distributed approach is implemented), but also improving the spread of solutions over the Pareto front (the ideal set of solutions). In order to do this, colony-level (coarse- grained) implementations have been tested for solving the Bicriteria TSP problem, yielding better sets of solutions, in the sense explained above, than a sequential approach.

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A Study of Parallel Approaches in MOACOs for Solving the Bicriteria TSP A.M. Mora , J.J. Merelo, P.A. Castillo, M.G. Arenas, P. García-Sánchez, J.L.J. Laredo Departamento de Arquitectura y Tecnología de Computadores. UNIVERSIDAD DE GRANADA International Work-Conference on Artificial Neural Networks 2011
INDEX ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],Ant Colony Optimization (I) BIO-INSPIRATION
Ant Colony Optimization (II) Pheromone trail ? CHOOSING A PATH
Ant Colony Optimization (III) ,[object Object],[object Object],[object Object],[object Object],MAIN FEATURES
Multi-Objective Problems ,[object Object],[object Object],[object Object],[object Object],DEFINITION ,[object Object]
Bicriteria TSP ,[object Object],[object Object],[object Object],THE PROBLEM
Parallelization (I) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],AIM
Parallelization (II) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],APPROACHES
[object Object],Experiments and Results (I) BIANT
[object Object],Experiments and Results (II) MOACS
[object Object],Experiments and Results (III) MOACS (Pareto Set comparisons) Solutions in each one of the processors Global Pareto Set
[object Object],Experiments and Results (IV) Non-dominated solutions Number of non-dominated solutions in the global Pareto Set
[object Object],Experiments and Results (V) Running Time Scalability graph
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Conclusions and Future Work
[object Object],The End

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A Study of Parallel Approaches in MOACOs for solving the Bicriteria TSP

  • 1. A Study of Parallel Approaches in MOACOs for Solving the Bicriteria TSP A.M. Mora , J.J. Merelo, P.A. Castillo, M.G. Arenas, P. García-Sánchez, J.L.J. Laredo Departamento de Arquitectura y Tecnología de Computadores. UNIVERSIDAD DE GRANADA International Work-Conference on Artificial Neural Networks 2011
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  • 4. Ant Colony Optimization (II) Pheromone trail ? CHOOSING A PATH
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Editor's Notes

  1. These are the non-dominates solutions yielded in each part of the Pareto Front (search space) Better than the Mono-Processor approach Finds solutions in the edges of the PF
  2. Again the set of non-dominated solutions per processor Better than the Mono-Processor approach Finds solutions in the edges of the PF
  3. Using a different ACO in each of the colonies