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      A Review of Multi-Criteria Decision-Making Methods Applied to the Sustainable Bridge Design

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      Sustainability

      MDPI AG

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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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            A New Heuristic Optimization Algorithm: Harmony Search

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              The analytic hierarchy process—what it is and how it is used

               R.W. Saaty (1987)
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                Author and article information

                Journal
                SUSTDE
                Sustainability
                Sustainability
                MDPI AG
                2071-1050
                December 2016
                December 09 2016
                : 8
                : 12
                : 1295
                Article
                10.3390/su8121295
                © 2016
                Product
                Self URI (article page): http://www.mdpi.com/2071-1050/8/12/1295

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