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Area of Science:

  • Computational Intelligence
  • Operations Research
  • Artificial Intelligence

Background:

  • Ant Colony Optimization (ACO) is effective for combinatorial problems like the Traveling Salesman Problem (TSP).
  • Standard ACO suffers from local minima and slow convergence.
  • Simulated Annealing (SA), mutation operators, and local search offer improvements in global convergence and convergence speed.

Purpose of the Study:

  • To develop a hybrid ACO algorithm for solving the Traveling Salesman Problem (TSP).
  • To leverage the strengths of ACO, SA, mutation operators, and local search.
  • To address the limitations of basic ACO, specifically local optima and convergence rate.

Main Methods:

  • A hybrid algorithm combining ACO with Simulated Annealing (SA) and a local search procedure.
  • SA and mutation operators are employed to enhance population diversity.
  • Local search is utilized for efficient exploitation of the search space.

Main Results:

  • The proposed hybrid ACO algorithm demonstrated superior performance on 24 TSP instances from TSPLIB.
  • Experimental results indicate improved solution quality compared to established algorithms.
  • The integration of SA and local search effectively mitigated ACO's convergence issues.

Conclusions:

  • The hybrid ACO algorithm offers a robust and effective approach for solving the Traveling Salesman Problem.
  • The synergistic combination of ACO with SA and local search significantly enhances optimization capabilities.
  • This method provides a valuable advancement for complex combinatorial optimization tasks.