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Dynamic Flying Ant Colony Optimization (DFACO) for Solving the Traveling Salesman Problem.

Fadl Dahan1, Khalil El Hindi2, Hassan Mathkour3

  • 1Department of Information System, College of Computer Engineering and Science, Prince Sattam Bin Abdulaziz University, Al Kharj 11942, Saudi Arabia. f.naji@psau.edu.sa.

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A new dynamic flying ant colony optimization (DFACO) algorithm effectively solves the traveling salesman problem (TSP). DFACO improves upon existing methods by balancing exploration and exploitation, avoiding local minima for better solutions.

Keywords:
ant colony optimization (ACO)dynamic flying ant colony optimization (DFACO)flying ant colony optimization (FACO)traveling salesman problem (TSP)

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Area of Science:

  • Artificial Intelligence
  • Optimization Algorithms
  • Computational Science

Background:

  • The traveling salesman problem (TSP) is a well-known combinatorial optimization challenge.
  • Existing algorithms often suffer from stagnation, limiting their effectiveness.
  • The flying ant colony optimization (FACO) algorithm, initially for QoS-aware web service selection, offers a novel approach to pheromone deposition.

Purpose of the Study:

  • To adapt and enhance the FACO algorithm for solving the TSP.
  • To develop a dynamic version of FACO (DFACO) that mitigates stagnation and improves solution quality.
  • To evaluate the performance of DFACO against established algorithms on benchmark TSP datasets.

Main Methods:

  • Modification of FACO to incorporate dynamic pheromone distribution based on solution quality.
  • Integration of the 3-Opt algorithm to refine solutions and combat stagnation.
  • Hybridization of regular and flying ants within the colony structure.
  • Comparative analysis of DFACO against Ant Colony Optimization (ACO) and parallel ACO (PACO)-3Opt.

Main Results:

  • DFACO significantly outperformed ACO and five other methods on 23 out of 24 TSP datasets in solution quality.
  • DFACO demonstrated superior performance compared to PACO-3Opt on 20 out of 21 datasets, excelling in both solution quality and execution time.
  • The modifications successfully balanced exploration and exploitation strategies, reducing the likelihood of getting trapped in local minima.

Conclusions:

  • The proposed dynamic flying ant colony optimization (DFACO) algorithm is a highly effective method for solving the traveling salesman problem.
  • DFACO offers a robust solution that overcomes the stagnation issues prevalent in many TSP algorithms.
  • The algorithm achieves superior results in terms of solution quality and processing efficiency, making it a valuable advancement in optimization techniques.