V. Maniezzo and A. Carbonaro. Ant Colony Optimization: an Overview. In Proceedings of MIC'99, III Metaheuristics International Conference, Brazil., 1999. Home/Search Document Details and Download
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The Influence of Run-Time Limits on Choosing Ant System Parameters - Socha (2003) (Correct)
....with a constant value of # fixed = 1.0, which was also more e#cient than the calculation of exact value based on quality of the solution. 4 Influence of Local Search It has been shown in the literature that ant algorithms perform particularly well, when supported by a local search (LS) routine [2, 9, 10]. There were also attempts to design the local search for the particular problem tackled here (the UCTP) 11] Here, we try to show that although adding an LS to an algorithm improves the results obtained, it is important to carefully choose the type of such LS routine, especially with regard to ....
Maniezzo, V., Carbonaro, A.: Ant Colony Optimization: an Overview. In Ribeiro, C., ed.: Essays and Surveys in Metaheuristics, Kluwer Academic Publishers (2001)
Learning Classifier Systems in Autonomous Agent Control Tasks - Borisov, Vasilyev (2002) (Correct)
....but searches for food by using the second pheromone ph2. After the food is found, it lays ph2, and searches for the nest by using phi. In this case the agent does not have a memory. To prevent recirculation in the case when the agent s departure point is not an impasse, we will use a tabu list [6]. The tabu list for each state is the state from which agent k has just come. tabu = s . Islands of colonies are used in the case of a complex model, i.e. when we have a problem of high dimensionality. The colonies work on the same problem independently and synchronize the best solutions ....
Maniezzo, V. & Carbonaro, A. Ant Colony Optimization: an Overview. Proceedings of MIC' 99, III Metaheuristics International Conference, Brazil, 1999.
Ant Algorithms: Review and Future Applications - Krishnan Krishnaiyer Hossein (Correct)
....algorithm produced a maximum yield of 70 , which is 30 more than result of the genetic algorithm. This clearly indicates the goodness of ant algorithms and its practical application. Many applications for the job shop scheduling problems can be found in the literature. Maniezzo and Carbonaro [15] reviewed the various ant algorithms under the framework of Ant Colony Optimization (ACO) algorithm. They also provide a comparative results obtained by the applications of ACO algorithm to many combinatorial optimization problems including job shop scheduling and vehicle routing. Zwaan and ....
Maniezzo, V., and Carbonaro, A., 1999, "Ant Colony Optimization: An Overview", Proceedings of MIC'99 III Metaheuristics International Conference, Brazil.
Learning Classifier Systems in Autonomous Agent Control Tasks - Borisov, Vasilyev (2002) (Correct)
....phi, but searches for food by using the second pheromone ph: After the food is found, it lays ph: and searches for the nest by using phi. In this case the agent does not have a memory. To prevent recirculation in the case when the agent s departure point is not an impasse, we will use tabu list [7]. Tabu list for each state is the state from which the agent k has just come. tabu = s . Islands of colonies are used in the case of a complex model, i.e. when we have a problem of high dimensionality. The colonies work on the same problem independently, and synchronize the best solutions ....
Maniezzo, V. & Carbonaro, A. Ant Colony Optimization: an Overview. Proceedings of MIC' 99, III Metaheuristics International Conference, Brazil, 1999.
Ant Colony Optimization - Maniezzo, Gambardella, de Luigi (2004) (3 citations) Self-citation (Maniezzo) (Correct)
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V. Maniezzo, A.Carbonaro (2001), Ant Colony Optimization: an overview, in C.Ribeiro (eds.) Essays and Surveys in Metaheuristics , Kluwer, pag.21-44.
An ANT Heuristic for the Frequency Assignment Problem - Maniezzo, al. (1999) (3 citations) Self-citation (Maniezzo) (Correct)
....as portable phones, has a tremendous impact on everyday life. Mobility raises a number of research questions: for many of them discrete models and algorithms are required in order to solve the underlying mathematical problem. The Ant Colony Optimization paradigm (ACO) Dorigo and Di Caro, 1999] [Maniezzo and Carbonaro, 1999] has proved successful in dealing with discrete optimization problems, for example with the travelling salesman, the quadratic assignment, the graph coloring and other problems. This paper presents its application to one of the main problems arising in mobile 2 telecommunication, namely the ....
....application of the ANTS metaheuristic to the radio link frequency assignment problem, with the objective of minimizing the total interference of an assignment plan. While ANTS has already proved to be effective on problems for which substantial results on lower bounding techniques are available [Maniezzo and Carbonaro, 1999], it was never tested on problems for which these results are not available. This is the case of the problem examined. Despite the weakness of the search guidance that the procedure can exploit, the computational results report a good global performance, thereby testifying the robustness of the ....
Maniezzo V. and Carbonaro A. (1999). Ant Colony Optimization: an Overview. Proceedings of MIC'99, III Metaheuristics International Conference, Brazil.
CSAA: A Constraint Satisfaction Ant Algorithm Framework - Mertens, Holvoet (2004) (Correct)
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V. Maniezzo and A. Carbonaro. Ant Colony Optimization: an Overview. In Proceedings of MIC'99, III Metaheuristics International Conference, Brazil., 1999.
Class Notes, Spring 2003 for: - Agents Games Evolution (Correct)
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V. Maniezzo and A. Carbonaro. Ant colony optimization: An overview. In C. Ribeiro, editor, Essays and Surveys in Metaheuristics, pages 21--44. Kluwer, 2001. Available at: http://www.csr.unibo.it/maniezzo/papers/ANTStut.pdf.
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