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Titlebook: Bio-Inspired Computing: Theories and Applications; 18th International C Linqiang Pan,Yong Wang,Jianqing Lin Conference proceedings 2024 The

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41#
發(fā)表于 2025-3-28 16:36:13 | 只看該作者
42#
發(fā)表于 2025-3-28 20:26:53 | 只看該作者
43#
發(fā)表于 2025-3-29 00:43:42 | 只看該作者
,Kunst — Heilmittel der Medizin,proposed. It introduces a novel mutation mode considering search directions is proposed firstly. Secondly, a levy flight strategy is employed to enhance the exploration capability of Differential Evolution (DE). Lastly, the Q-learning method from reinforcement learning is introduced to establish a s
44#
發(fā)表于 2025-3-29 05:14:21 | 只看該作者
Der Mensch Ijob redet mit Gott,global search, a hierarchical competitive differential evolution algorithm is proposed. It uniquely incorporates a hierarchical competition mechanism and an adaptive differential mutation strategy based on competition outcomes, substantially enhancing global search. The proposed algorithm benchmarks
45#
發(fā)表于 2025-3-29 10:04:16 | 只看該作者
Das Menschenbild in der Softwareentwicklung,bject to constrained limitations, they become dynamic constrained multi-objective optimization problems (DCMOPs). As the problems become more complex, multi-objective optimization algorithms face greater challenges. In this paper, a hybrid response strategy for dynamic constrained multi-objective op
46#
發(fā)表于 2025-3-29 12:53:02 | 只看該作者
,Richtig verhandeln – die Harvard Methode,ver time. The challenge in solving DMOPs is how to quickly track the Pareto optimal solution set when the environment changes. Recently, dynamic multi-objective evolutionary algorithms (DMOEAs) combined with transfer learning (TL) have been proven to be promising in solving DMOPs. TL-based DMOEAs sh
47#
發(fā)表于 2025-3-29 17:04:39 | 只看該作者
48#
發(fā)表于 2025-3-29 21:38:51 | 只看該作者
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發(fā)表于 2025-3-30 03:02:42 | 只看該作者
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發(fā)表于 2025-3-30 06:14:01 | 只看該作者
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