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 TSP

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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 ...

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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  • 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
  • Again the set of non-dominated solutions per processor Better than the Mono-Processor approach Finds solutions in the edges of the PF
  • Using a different ACO in each of the colonies

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

  • 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
    • Ant Colony Optimization
    • Multi-Objective Problems
    • Bicriteria TSP
    • Parallelization
    • Experiments and Results
    • Conclusions and Future Work
    • 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
  • Ant Colony Optimization (II) Pheromone trail ? CHOOSING A PATH
  • Ant Colony Optimization (III)
    • 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
    MAIN FEATURES
  • Multi-Objective Problems
    • 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
    DEFINITION
    • There is a set of solutions, the Pareto Set
  • Bicriteria TSP
    • 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
    THE PROBLEM
  • Parallelization (I)
    • Two profits:
      • Yield better results
      • Improve the running time
    • Two Schemes (at colony level):
      • Space specialized colonies
      • Objective specialized colonies
    AIM
  • Parallelization (II)
    • 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
    APPROACHES
    • Bi-criteria KROA-100 Problem. 16 processors
    Experiments and Results (I) BIANT
    • Bi-criteria KROA-100 Problem. 16 processors
    Experiments and Results (II) MOACS
    • 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
    • Bi-criteria KROA-100 Problem. 16 processors
    Experiments and Results (IV) Non-dominated solutions Number of non-dominated solutions in the global Pareto Set
    • Bi-criteria KROA-100 Problem. 16 processors
    Experiments and Results (V) Running Time Scalability graph
    • 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
    • Thank You!
    The End