Jozef Kratica, Dušan Tošic, Vladimir Filipović, Ivana Ljubić (2001)
RAIRO - Operations Research - Recherche Opérationnelle
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The simple plant location problem (SPLP) is considered and a genetic algorithm is proposed to solve this problem. By using the developed algorithm it is possible to solve SPLP with more than 1000 facility sites and customers. Computational results are presented and compared to dual based algorithms.
Arumugam, M.Senthil, Rao, M.V.C. (2006)
Discrete Dynamics in Nature and Society
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Chen, Hanning, Zhu, Yunlong, Hu, Kunyuan (2011)
Abstract and Applied Analysis
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Nacima Labadi, Christian Prins, Mohamed Reghioui (2008)
RAIRO - Operations Research
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This article deals with the vehicle routing problem with time windows (VRPTW). This problem consists in determining a least-cost set of trips to serve customers during specific time windows. The proposed solution method is a memetic algorithm (MA), a genetic algorithm hybridised with a local search. Contrary to most papers on the VRPTW, which minimize first the number of vehicles, our method is also able to minimize the total distance travelled. The results on 56 classical instances...
Rafał Szłapczyński, Joanna Szłapczyńska (2012)
International Journal of Applied Mathematics and Computer Science
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The paper presents selected aspects of evolutionary sets of safe ship trajectories-a method which applies evolutionary algorithms and some of the assumptions of game theory to solving ship encounter situations. For given positions and motion parameters of the ships, the method finds a near optimal set of safe trajectories of all ships involved in an encounter. The method works in real time and the solutions must be returned within one minute, which enforces speeding up the optimisation...
Chen, Hanning, Zhu, Yunlong, Hu, Kunyuan (2009)
Discrete Dynamics in Nature and Society
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Jozef Kratica, Dušan Tošić, Vladimir Filipović, Đorđe Dugošija (2011)
The Yugoslav Journal of Operations Research
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Oscar Cordón, Francisco Herrera, Thomas Stützle (2002)
Mathware and Soft Computing
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Ant Colony Optimization (ACO) is a recent metaheuristic method that is inspired by the behavior of real ant colonies. In this paper, we review the underlying ideas of this approach that lead from the biological inspiration to the ACO metaheuristic, which gives a set of rules of how to apply ACO algorithms to challenging combinatorial problems. We present some of the algorithms that were developed under this framework, give an overview of current applications, and analyze the relationship...