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Titlebook: Advances in Metaheuristics for Hard Optimization; Patrick Siarry,Zbigniew Michalewicz Book 2008 Springer-Verlag Berlin Heidelberg 2008 Hid

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51#
發(fā)表于 2025-3-30 08:43:32 | 只看該作者
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發(fā)表于 2025-3-30 13:45:55 | 只看該作者
Cecilia Hidalgo,Claudia E. Natenzonchnique: Simulated Annealing. The technique allows one to define n desired path points to be followed by a four-bar linkage (path generation problem). The synthesis problem is transformed into an optimization problem in order to use the Simulated Annealing algorithm. With this approach, a path can b
53#
發(fā)表于 2025-3-30 16:50:47 | 只看該作者
Greening of Industry Networks Studiesmulti-star tabu search (TS) and convex combinationmethods as a diversification generationmethod. A new mechanism of forbidden solutions to create different families of subsets of solutions to be combined, is applied in this version. Aconstrain-handlingmechanism is incorporated to deal with constrain
54#
發(fā)表于 2025-3-30 21:42:21 | 只看該作者
Tuuli Mattelm?ki,Andrés Lucero,Jung-Joo Leeation in obtaining good results with this technique is the choice of distance function, and correspondingly which features to consider when computing distances between samples. In this chapter, a new ensemble technique is proposed to improve the performance of NNclassifiers.The proposed approach com
55#
發(fā)表于 2025-3-31 03:51:31 | 只看該作者
56#
發(fā)表于 2025-3-31 08:31:34 | 只看該作者
Crowdsourcing User and Design Researche consumer packaged goods industry. The multistage manufacturing plant is comprised of different stages: mixing, storage, packing and finished goods storage, and is an extension of the classic Flowshop Scheduling Problem (FSP).We propose a new algorithm for the Multistage Flowshop Scheduling Problem
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發(fā)表于 2025-3-31 12:57:06 | 只看該作者
58#
發(fā)表于 2025-3-31 13:39:21 | 只看該作者
59#
發(fā)表于 2025-3-31 19:35:33 | 只看該作者
60#
發(fā)表于 2025-4-1 01:44:14 | 只看該作者
Initial Knowledge Sharing in Outsourcing for fine-tuning solutions, which are very close to optimal ones. However, genetic algorithms may be specifically designed to provide an effective local search as well. In fact, several genetic algorithm models have recently been presented with this aim. In this chapter, we call these algorithms Loc
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