Ant system: optimization by a colony of cooperating agents

@article{Dorigo1996AntSO,
  title={Ant system: optimization by a colony of cooperating agents},
  author={Marco Dorigo and Vittorio Maniezzo and Alberto Colorni},
  journal={IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society},
  year={1996},
  volume={26 1},
  pages={
          29-41
        }
}
  • M. DorigoV. ManiezzoA. Colorni
  • Published 1 February 1996
  • Computer Science
  • IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society
An analogy with the way ant colonies function has suggested the definition of a new computational paradigm, which we call ant system (AS. [] Key Method We apply the proposed methodology to the classical traveling salesman problem (TSP), and report simulation results. We also discuss parameter selection and the early setups of the model, and compare it with tabu search and simulated annealing using TSP. To demonstrate the robustness of the approach, we show how the ant system (AS) can be applied to other…
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11,748 Citations
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Background Citations
3,335
Methods Citations
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11,748 Citations

Ant Colony System: A Cooperative Learning Approach to the Traveling Salesman Problem

The results show that ACS outperforms other nature-inspired algorithms such as simulated annealing and evolutionary computation, and it is concluded comparing ACS-3-opt, a version of ACS augmented with a local search procedure, to some of the best performing algorithms for symmetric and asymmetric TSPs.

An improved ant colony optimization algorithm for solving a complex combinatorial optimization problem

The use of ant colony optimization algorithms for the problem of optimal route search

The ant colony system is introduced, a distributed algorithm that is applied to the traveling salesman problem, where a set of cooperating agents called ants cooperate to find good solutions to traveling salesman problems.

Ant colony system: a cooperative learning approach to the traveling salesman problem

The results show that the ACS outperforms other nature-inspired algorithms such as simulated annealing and evolutionary computation, and it is concluded comparing ACS-3-opt, a version of the ACS augmented with a local search procedure, to some of the best performing algorithms for symmetric and asymmetric TSPs.

Industrial applications of the ant colony optimization algorithm

A hybridization using iterated local search (ILS) is made in this work to the existing heuristic to refine the optimality of the solution.

Solving TSP Using Improved Elitist Ant System Based on Improved Pheromone Strategy and Dynamic Candidate List

An improved ant colony optimization algorithm is proposed with two highlights: first, candidate set strategy is adapted to rapid convergence speed and second, the adaptive adjustment pheromone strategy is used to make relatively uniform peromone distribution to balance the exploration and exploitation between the random search of ant.

An Effective Hybrid Ant Colony Algorithm for Solving the Traveling Salesman Problem

  • Liu WeiZhou Yuren
  • Computer Science
    2010 International Conference on Intelligent Computation Technology and Automation
  • 2010
A hybrid ACO algorithm for the TSP to overcome some shortcomings of the prior ACO and take advantage of the relationship between the MST and the optimal path to limit the search range of the ant in each city.

Solving the traveling salesman problem using cooperative genetic ant systems

The Nearest Neighbor Ant Colony System: A Spatially-Explicit Algorithm for the Traveling Salesman Problem

In this chapter, a new heuristic algorithm called the nearest neighbor ant colony system (NNAC) is proposed in order to reduce computing time, without sacrificing on the optimality properties of the solutions.

An Ant System-Assisted Genetic Algorithm For Solving The Traveling Salesman Problem

A new hybrid algorithm, ant system-assisted genetic algorithm (ASaGA) to handle the travelling salesman problem (TSP) by using the results of ACO to replace that of GA after every certain number of runs during the process.
...

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