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Titlebook: Bio-Inspired Computing: Theories and Applications; 15th International C Linqiang Pan,Shangchen Pang,Faming Gong Conference proceedings 2021

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發(fā)表于 2025-3-21 16:09:57 | 只看該作者 |倒序瀏覽 |閱讀模式
期刊全稱Bio-Inspired Computing: Theories and Applications
期刊簡稱15th International C
影響因子2023Linqiang Pan,Shangchen Pang,Faming Gong
視頻videohttp://file.papertrans.cn/187/186343/186343.mp4
學(xué)科分類Communications in Computer and Information Science
圖書封面Titlebook: Bio-Inspired Computing: Theories and Applications; 15th International C Linqiang Pan,Shangchen Pang,Faming Gong Conference proceedings 2021
影響因子This volume constitutes the revised selected papers of the 15th International Conference on Bio-inspired Computing: Theories and Applications, BIC-TA 2020, held in Qingdao, China, in October? 2020..The 43 full papers presented in both volumes were selected from 109 submissions. The papers are organized according to the topical headings: evolutionary computation and swarm intelligence; neural networks and machine learning; DNA computing and membrane computing.?.
Pindex Conference proceedings 2021
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Incorporating the Confusion Effect into the Simulated Evolution of Crowded Selfish Herdsconfusion effect is restrained by intraspecific competition. Secondly, highly coordinated movements are primary stable states, which geometry is moulded by the degree of the confusion effect. Lastly, a strong confusion effect promotes a branch of stable states where selfish herds exercise as swarms
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Schiffbautechnische Gesellschaftaw materials more effective. Finally, a multi-objective optimization model is established. According to the contribution rate of the raw material index of the three products, the information entropy method is used to calculate the weight of the raw material index corresponding to the three products,
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https://doi.org/10.1007/978-3-642-92039-4rk instances as testing sets and the simulated results show that HSA-DA outperforms the compared algorithms in some benchmark instances, which verified the feasibility of the algorithm in solving multi-objective VRPTW.
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