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Titlebook: Recent Advances in Computational Optimization; Stefka Fidanova Book 2013 Springer International Publishing Switzerland 2013 Computational

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21#
發(fā)表于 2025-3-25 06:48:35 | 只看該作者
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發(fā)表于 2025-3-25 08:43:45 | 只看該作者
Experimental Evaluation of Pheromone Structures for Ant Colony Optimization: Application to the Roboblem is NP-hard. On the basis of previous work [3], [37], we solve the skin wiring problem using an Ant Colony Optimization approach. In this Chapter, we address the problem of designing a good pheromone structure: we propose five alternatives, which are validated using both real and artificially generated problem instances.
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發(fā)表于 2025-3-25 12:20:21 | 只看該作者
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發(fā)表于 2025-3-26 02:31:43 | 只看該作者
Homogeneous Non Idling Problems: Models and Algorithms,blem. The last part of the paper is devoted to the Linear Cost Minimization of multiprocessor scheduling with non idling constraints, which we handle through linear model and a heuristic Lagrangean decomposition approach, and involves numerical experiments.
27#
發(fā)表于 2025-3-26 06:38:07 | 只看該作者
Book 2013imization of the profit, quality and efficiency..Also many real world problems arising in engineering, economics, medicine and other domains can be formulated as optimization tasks..This volume is a comprehensive collection of extended contributions from the Workshop on Computational Optimization..T
28#
發(fā)表于 2025-3-26 10:37:48 | 只看該作者
A New Hybrid GA-FA Tuning of PID Controller for Glucose Concentration Control,n the basis of the considered PID controllers tuning procedures. Moreover, the observed results are compared to the ones obtained by applying the pure FA and pure GA. The comparison shows that the proposed hybrid algorithm is highly competitive with standard FA and GA for considered here optimization problem.
29#
發(fā)表于 2025-3-26 14:01:26 | 只看該作者
30#
發(fā)表于 2025-3-26 18:01:22 | 只看該作者
A Graph Optimization Approach to Item-Based Collaborative Filtering,t they need or prefer. Various approaches for recommender systems have been developed that utilize either demographic, content, or historical information. Among these methods, item-based collaborative filtering is one of most widely used and successful neighborhood-based collaborative recommendation
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