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Titlebook: Nature Inspired Cooperative Strategies for Optimization (NICSO 2010); Juan R. González,David Alejandro Pelta,Natalio Kra Book 20101st edit

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31#
發(fā)表于 2025-3-27 00:17:21 | 只看該作者
Studying the Influence of the Objective Balancing Parameter in the Performance of a Multi-ObjectiveHAC, and two other algorithms from the literature, which have been adapted to solve the same problem. The experiments show that the use of a variable value for . yields a wider Pareto set, but keeping a constant value for this parameter let to find better results for any objective.
32#
發(fā)表于 2025-3-27 04:23:16 | 只看該作者
HC12: Highly Scalable Optimisation Algorithm,er aims to show that HC12 is highly scalable and can be implemented in a cluster of computers. As a practical consequence, the high scalability substantially reduces the computing time of optimisation problems.
33#
發(fā)表于 2025-3-27 05:25:46 | 只看該作者
Evolutionary Algorithms for Planar MEMS Design Optimisation: A Comparative Study,ve investigation into the performance of these two MOEA on a number of MEMS design optimisation case studies. MOGA-II proved to be superior to NSGA-II. Experiments are, herein, described and results are discussed.
34#
發(fā)表于 2025-3-27 10:56:55 | 只看該作者
A Distributed Service Oriented Framework for Metaheuristics Using a Public Standard, event administration, easy service implementation, transparent service distribution and lifecycle management. In this work, a framework based in OSGi is presented, and as an example two heuristics have been developed: a Tabu Search and a Distributed Genetic Algorithm.
35#
發(fā)表于 2025-3-27 16:05:58 | 只看該作者
36#
發(fā)表于 2025-3-27 19:43:00 | 只看該作者
37#
發(fā)表于 2025-3-27 23:27:58 | 只看該作者
Fault Diagnosis in Industrial Systems Using Bioinspired Cooperative Strategies,nks system. The experiments have considered noisy data in order to compare the robustness of the diagnosis. The preliminary results indicate that the proposed approach, basically the combination of the two algorithms, characterizes a promising methodology for the Fault Detection and Isolation problem.
38#
發(fā)表于 2025-3-28 02:11:39 | 只看該作者
,Eagle Strategy Using Lévy Walk and Firefly Algorithms for Stochastic Optimization, strategy intends to combine the random search using Lévy walk with the firefly algorithm in an iterative manner. Numerical studies and results suggest that the proposed Eagle Strategy is very efficient for stochastic optimization. Finally practical implications and potential topics for further research will be discussed.
39#
發(fā)表于 2025-3-28 09:25:39 | 只看該作者
40#
發(fā)表于 2025-3-28 11:28:26 | 只看該作者
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