A Study of Parallel Approaches in MOACOs for Solving the  Bicriteria TSP A.M. Mora , J.J. Merelo, P.A. Castillo,  M.G. Are...
INDEX <ul><li>Ant Colony Optimization </li></ul><ul><li>Multi-Objective Problems </li></ul><ul><li>Bicriteria TSP </li></u...
<ul><li>Metaheuristic  inspired in the behaviour of ants  when search for food </li></ul><ul><li>They ‘build’ (after some ...
Ant Colony Optimization (II) Pheromone trail ? CHOOSING A PATH
Ant Colony Optimization (III) <ul><li>A set of  independent artificial agents </li></ul><ul><li>They  move in graphs  sear...
Multi-Objective Problems <ul><li>There are  some functions to optimise </li></ul><ul><li>One  solution  must satisfy  some...
Bicriteria TSP <ul><li>Search for the  Hamiltonian circuit which minimizes the cost  of the edges to go through (distance)...
Parallelization (I) <ul><li>Two profits: </li></ul><ul><ul><li>Yield  better results </li></ul></ul><ul><ul><li>Improve th...
Parallelization (II) <ul><li>BIANT   (by Iredi et al.) </li></ul><ul><ul><li>It is an  Ant System </li></ul></ul><ul><ul><...
<ul><li>Bi-criteria KROA-100 Problem. 16 processors </li></ul>Experiments and Results (I) BIANT
<ul><li>Bi-criteria KROA-100 Problem. 16 processors </li></ul>Experiments and Results (II) MOACS
<ul><li>Bi-criteria KROA-100 Problem. 11 processors </li></ul>Experiments and Results (III) MOACS (Pareto Set comparisons)...
<ul><li>Bi-criteria KROA-100 Problem. 16 processors </li></ul>Experiments and Results (IV) Non-dominated solutions Number ...
<ul><li>Bi-criteria KROA-100 Problem. 16 processors </li></ul>Experiments and Results (V) Running Time Scalability graph
<ul><li>Two well-known MOACOs  have been implemented in a parallel shape. </li></ul><ul><li>Two different searching scheme...
<ul><li>Thank You! </li></ul>The End
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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 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

    1. 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
    2. 2. INDEX <ul><li>Ant Colony Optimization </li></ul><ul><li>Multi-Objective Problems </li></ul><ul><li>Bicriteria TSP </li></ul><ul><li>Parallelization </li></ul><ul><li>Experiments and Results </li></ul><ul><li>Conclusions and Future Work </li></ul>
    3. 3. <ul><li>Metaheuristic inspired in the behaviour of ants when search for food </li></ul><ul><li>They ‘build’ (after some time) the shortest paths between the nest and the food sources </li></ul><ul><li>They communicate using the environment </li></ul><ul><li>Depositing and following pheromone </li></ul>Ant Colony Optimization (I) BIO-INSPIRATION
    4. 4. Ant Colony Optimization (II) Pheromone trail ? CHOOSING A PATH
    5. 5. Ant Colony Optimization (III) <ul><li>A set of independent artificial agents </li></ul><ul><li>They move in graphs searching for solutions </li></ul><ul><li>They use a pheromone matrix to decide where to move </li></ul><ul><li>In addition, they consider heuristic functions to build the solutions </li></ul>MAIN FEATURES
    6. 6. Multi-Objective Problems <ul><li>There are some functions to optimise </li></ul><ul><li>One solution must satisfy some criteria </li></ul><ul><li>Dominance concept: </li></ul><ul><li>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 </li></ul>DEFINITION <ul><li>There is a set of solutions, the Pareto Set </li></ul>
    7. 7. Bicriteria TSP <ul><li>Search for the Hamiltonian circuit which minimizes the cost of the edges to go through (distance) </li></ul><ul><li>There is a MO-TSP , which has associated some costs to each edge </li></ul><ul><li>The Bicriteria TSP considered two costs </li></ul>THE PROBLEM
    8. 8. Parallelization (I) <ul><li>Two profits: </li></ul><ul><ul><li>Yield better results </li></ul></ul><ul><ul><li>Improve the running time </li></ul></ul><ul><li>Two Schemes (at colony level): </li></ul><ul><ul><li>Space specialized colonies </li></ul></ul><ul><ul><li>Objective specialized colonies </li></ul></ul>AIM
    9. 9. Parallelization (II) <ul><li>BIANT (by Iredi et al.) </li></ul><ul><ul><li>It is an Ant System </li></ul></ul><ul><ul><li>Two pheromone matrices </li></ul></ul><ul><ul><li>Two heuristic functions </li></ul></ul><ul><ul><li> parameter to weight the objectives </li></ul></ul><ul><li>MOACS (by Gambardella and Barán et al.) </li></ul><ul><ul><li>It is an Ant Colony System </li></ul></ul><ul><ul><li>One pheromone matrix </li></ul></ul><ul><ul><li>Two heuristic and costs functions </li></ul></ul><ul><ul><li> parameter to weight the objectives </li></ul></ul>APPROACHES
    10. 10. <ul><li>Bi-criteria KROA-100 Problem. 16 processors </li></ul>Experiments and Results (I) BIANT
    11. 11. <ul><li>Bi-criteria KROA-100 Problem. 16 processors </li></ul>Experiments and Results (II) MOACS
    12. 12. <ul><li>Bi-criteria KROA-100 Problem. 11 processors </li></ul>Experiments and Results (III) MOACS (Pareto Set comparisons) Solutions in each one of the processors Global Pareto Set
    13. 13. <ul><li>Bi-criteria KROA-100 Problem. 16 processors </li></ul>Experiments and Results (IV) Non-dominated solutions Number of non-dominated solutions in the global Pareto Set
    14. 14. <ul><li>Bi-criteria KROA-100 Problem. 16 processors </li></ul>Experiments and Results (V) Running Time Scalability graph
    15. 15. <ul><li>Two well-known MOACOs have been implemented in a parallel shape. </li></ul><ul><li>Two different searching schemes have been applied (SSC and OSC). </li></ul><ul><li>Improving in the sets of solutions . </li></ul><ul><li>Improving in the running time . </li></ul><ul><li>Implement a heterogeneous scheme </li></ul><ul><li>Test other instances and problems </li></ul><ul><li>Implement an ant-level parallelization </li></ul><ul><li>Use a higher number of processors </li></ul>Conclusions and Future Work
    16. 16. <ul><li>Thank You! </li></ul>The End
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