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Titlebook: Advances in Swarm Intelligence; 15th International C Ying Tan,Yuhui Shi Conference proceedings 2024 The Editor(s) (if applicable) and The A

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发表于 2025-3-21 18:22:41 | 显示全部楼层 |阅读模式
期刊全称Advances in Swarm Intelligence
期刊简称15th International C
影响因子2023Ying Tan,Yuhui Shi
视频video
学科分类Lecture Notes in Computer Science
图书封面Titlebook: Advances in Swarm Intelligence; 15th International C Ying Tan,Yuhui Shi Conference proceedings 2024 The Editor(s) (if applicable) and The A
影响因子.This two-volume set LNCS 14788 and 14789 constitutes the refereed post-conference proceedings of the 15th International Conference on Advances in Swarm Intelligence, ICSI 2024, held in Xining, China, during August 23–26, 2024...The 74 revised full papers presented in these proceedings were carefully reviewed and selected from 156 submissions. The papers are organized in the following topical sections:..Part I - Particle swarm optimization; Swarm intelligence computing; Differential evolution; Evolutionary algorithms; Multi-agent reinforcement learning & Multi-objective optimization...Part II - Route planning problem; Machine learning; Detection and prediction; Classification; Edge computing; Modeling and optimization & Analysis of review..
Pindex Conference proceedings 2024
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Schwimmkasten- und Druckluftgründungenlem and meet specific purposes. The current status of swarm intelligence on planning and scheduling tasks is discussed and the challenges of swarm intelligence and automated planning algorithms design are analyzed in this paper. Interpretability is the foundation of understanding problems’ propertie
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Grundlagen des Maschinenfundamententwurfs CPP methods assume reliable global communication but there is only local communication in many real-world scenarios. The related studies on robot collaboration under local communication face issues of excessive meeting cost or lack of collaboration when applied to CPP tasks. This paper proposes a G
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https://doi.org/10.1007/978-3-7091-8137-9o solve the above problem. Firstly, the problem is formulated as a multi-constrained optimization problem, incorporating constraints on drone performance and environmental conditions. Secondly, a hyper-heuristic algorithm based on Q-learning (HHBQL) is introduced, which utilizes Q-learning (QL) as a
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Gründungsausbildung in Netzwerkenver pollution control. In this study, we focus on a typical river watershed in Nanjing, China. We collect and process historical monitoring data from multiple automated water quality stations within the basin. To predict water quality, we employ three time series models: Support Vector Machine (SVM)
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Zum Gegenstand der Untersuchung, fact and imposes constraints on model design, training performance and accuracy. This paper introduces a fine-grained federated learning (FGFL) method to tackle resource heterogeneity. FGFL utilizes a configurable architecture search space in order to offer abundant architectures for various device
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