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      Variable neighborhood search: Principles and applications

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      European Journal of Operational Research

      Elsevier BV

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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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            Tabu Search—Part I

             Fred Glover (1989)
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              Tabu Search—Part II

               Fred Glover (1990)
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                Author and article information

                Journal
                European Journal of Operational Research
                European Journal of Operational Research
                Elsevier BV
                03772217
                May 2001
                May 2001
                : 130
                : 3
                : 449-467
                Article
                10.1016/S0377-2217(00)00100-4
                © 2001

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