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Titlebook: Recent Advances in Computational Optimization; Results of the Works Stefka Fidanova Book 2019 Springer Nature Switzerland AG 2019 Computati

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41#
發(fā)表于 2025-3-28 17:29:51 | 只看該作者
Optimisation of Preparedness and Response of Health Services in Major Crises Using the IMPRESS Platystem components are presented, among the validation and optimization activities during the demonstrations implemented in Palermo, Italy (field test exercise), Podgorica, Montenegro (field test exercise) and Sofia, Bulgaria (table top exercise).
42#
發(fā)表于 2025-3-28 22:15:48 | 只看該作者
,Structure Optimization and Learning of?Fuzzy Cognitive Map with the Use of?Evolutionary Algorithm a based on metrics from the area of graph theory: significance of each node, total value of a node and total influence of the concept and determine the weights of the connections between them. A simulation analysis of the developed algorithm was done with the use of synthetic and real-life data.
43#
發(fā)表于 2025-3-29 01:35:14 | 只看該作者
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發(fā)表于 2025-3-29 03:51:57 | 只看該作者
45#
發(fā)表于 2025-3-29 10:43:49 | 只看該作者
The New Approach for Dynamic Optimization with Variability Constraints,igned procedure was based on the modified direct shooting method, which can transform the dynamic optimization problem into a large-scale nonlinear optimization task (NLP). The first-order KKT optimality conditions with complementarity constraints were obtained. Finally, to solve the optimality cond
46#
發(fā)表于 2025-3-29 14:25:02 | 只看該作者
Intercriteria Analysis of ACO Performance for Workforce Planning Problem, set of employees from a set of available workers and to assign this staff to the jobs to be performed. A workforce planning problem is very complex and requires special algorithms to be solved. The complexity of this problem does not allow the application of exact methods for instances of realistic
47#
發(fā)表于 2025-3-29 16:20:53 | 只看該作者
48#
發(fā)表于 2025-3-29 23:02:46 | 只看該作者
49#
發(fā)表于 2025-3-30 02:00:26 | 只看該作者
InterCriteria Analysis Approach for Comparison of Simple and Multi-population Genetic Algorithms Peple and multi-population genetic algorithms performance. Six kinds of simple genetic algorithms and six kinds of multi-population genetic algorithms, differing in the execution order of the main genetic operators selection, crossover and mutation are in the focus of current investigation. Intercrite
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