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Titlebook: Experimental and Efficient Algorithms; Third International Celso C. Ribeiro,Simone L. Martins Conference proceedings 2004 Springer-Verlag

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樓主: Malicious
11#
發(fā)表于 2025-3-23 09:45:53 | 只看該作者
12#
發(fā)表于 2025-3-23 15:00:02 | 只看該作者
Kirsten Hattermann,Rolf Mentleinoperties that make the heuristics efficient are established. We also present a mixed integer programming formulation of the problem whose linear relaxation yields tighter lower bounds than known formulations. Computational results obtained with our algorithms are compared with those from existing constructive heuristics on several types of graphs.
13#
發(fā)表于 2025-3-23 22:00:06 | 只看該作者
Raymond Y. Huang,Patrick Y. Wenobust solutions. However, the efficiency of algorithms based on Local Search, such as Tabu Search, suffers from the complexity of evaluating the objective function after each move..In this paper, we propose alternative methods of dealing with uncertainties which are suitable to be implemented within a Tabu Search framework.
14#
發(fā)表于 2025-3-24 01:40:48 | 只看該作者
15#
發(fā)表于 2025-3-24 06:02:59 | 只看該作者
Recent Results in Cancer Researchan have a huge negative impact on the clustering quality. In this paper, we apply a clustering method that does not require a priori knowledge. We demonstrate the effectiveness and efficiency of the method on real and synthetic data sets emulating solutions in Multimodal Optimisation problems.
16#
發(fā)表于 2025-3-24 08:29:35 | 只看該作者
17#
發(fā)表于 2025-3-24 14:05:30 | 只看該作者
18#
發(fā)表于 2025-3-24 17:04:22 | 只看該作者
https://doi.org/10.1007/978-3-031-35295-9ruction algorithm is proposed to build feasible solutions to the problem. Two neighborhood structures and a local search procedure for solution improvement are also proposed. Computational results are presented and discussed, illustrating the effectiveness of the combined approach involving randomized construction and local search.
19#
發(fā)表于 2025-3-24 20:40:11 | 只看該作者
20#
發(fā)表于 2025-3-25 02:33:02 | 只看該作者
An Algorithm to Identify Clusters of Solutions in Multimodal Optimisation,an have a huge negative impact on the clustering quality. In this paper, we apply a clustering method that does not require a priori knowledge. We demonstrate the effectiveness and efficiency of the method on real and synthetic data sets emulating solutions in Multimodal Optimisation problems.
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