斥责
发表于 2025-3-25 06:58:35
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vector
发表于 2025-3-25 10:55:09
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GRIEF
发表于 2025-3-25 12:09:42
Improving the Performance and Scalability of Differential Evolution in the mutation step to make efficient progress on non-separable problems. We present an enhancement to Differential Evolution that introduces greater diversity. The new DE approach demonstrates fast convergence towards the global optimum and is highly scalable in the decision space.
vascular
发表于 2025-3-25 15:55:29
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BOGUS
发表于 2025-3-25 21:58:13
https://doi.org/10.1007/978-3-540-89694-4Alife; Scheduling; adaptive systems; algorithms; ant colonies; ant colony optimization; ants; artificial li
Temporal-Lobe
发表于 2025-3-26 00:09:39
978-3-540-89693-7Springer-Verlag Berlin Heidelberg 2008
synovitis
发表于 2025-3-26 04:30:29
Simulated Evolution and Learning978-3-540-89694-4Series ISSN 0302-9743 Series E-ISSN 1611-3349
宽敞
发表于 2025-3-26 11:41:42
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dilute
发表于 2025-3-26 13:27:51
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Mortal
发表于 2025-3-26 19:27:52
A New Approach to Adapting Control Parameters in Differential Evolution Algorithmue and standard deviation is employed to generate new control parameters. Performance on a set of benchmark functions indicates that our proposed method converges fast and achieves competitive results.