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      A parallel cooperative hybrid method based on ant colony optimization and 3-Opt algorithm for solving traveling salesman problem

      , , ,
      Soft Computing
      Springer Science and Business Media LLC

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          Ant system: optimization by a colony of cooperating agents.

          An analogy with the way ant colonies function has suggested the definition of a new computational paradigm, which we call ant system (AS). We propose it as a viable new approach to stochastic combinatorial optimization. The main characteristics of this model are positive feedback, distributed computation, and the use of a constructive greedy heuristic. Positive feedback accounts for rapid discovery of good solutions, distributed computation avoids premature convergence, and the greedy heuristic helps find acceptable solutions in the early stages of the search process. 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 optimization problems like the asymmetric traveling salesman, the quadratic assignment and the job-shop scheduling. Finally we discuss the salient characteristics-global data structure revision, distributed communication and probabilistic transitions of the AS.
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            Ant colony system: a cooperative learning approach to the traveling salesman problem

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              TSPLIB—A Traveling Salesman Problem Library

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                Author and article information

                Journal
                Soft Computing
                Soft Comput
                Springer Science and Business Media LLC
                1432-7643
                1433-7479
                March 2018
                November 17 2016
                March 2018
                : 22
                : 5
                : 1669-1685
                Article
                10.1007/s00500-016-2432-3
                b0a1b8e0-ede2-4070-8935-8cf9a530770a
                © 2018

                http://www.springer.com/tdm

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