Multi-Agent Evolutionary Approach to the TSP


A new parallel and distributed algorithm is proposed for the Traveling Salesman Problem (TSP) based upon a Multi-agent Evolutionary Algorithm (MAEA). An agent is assigned to a single city and builds locally its neighborhood - a subset of cities, which are then considered as local candidates to a global solution of TSP. The global solution of the TSP problem is based on an Ant Colonies (AC)-like paradigm. The cycles that are found by the ants are placed in data structure called Global Table, and are evaluated by genetic algorithm (GA) to modify the rank of cities in local neighborhoods. We present a description of the algorithm and show how values of some parameters of MAEA influence on the quality of solutions. We present the results of an experimental study which shows that the proposed algorithm outperforms two other currently used metaheuristics - AC and artificial immune systems.

  • Abstract
  • Introduction
  • The Algorithm Concept
  • Multi-Agent Evolutionary Algorithm
  • Building Local Neighborhood
  • Creating Cycles
  • Experimental Results
  • Modification of Weights of Agents
  • Algorithms Results Comparison
  • Conclusions
  • References

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