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      An Ant Colony Optimization Approach to a Grid Workflow Scheduling Problem With Various QoS Requirements

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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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              Performance-effective and low-complexity task scheduling for heterogeneous computing

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                Author and article information

                Journal
                IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews)
                IEEE Trans. Syst., Man, Cybern. C
                Institute of Electrical and Electronics Engineers (IEEE)
                1094-6977
                1558-2442
                January 2009
                January 2009
                : 39
                : 1
                : 29-43
                Article
                10.1109/TSMCC.2008.2001722
                0732c033-c3f0-4223-9518-09b7093ba0b3
                © 2009
                History

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