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Tongyang Xu, Yuan Liu, Zhaotai Ma, Yiqiang Huang and Peng Liu
As a new distributed machine learning (ML) approach, federated learning (FL) shows great potential to preserve data privacy by enabling distributed data owners to collaboratively build a global model without sharing their raw data. However, the heterogen...
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Sirui Chen, Yuming Tian and Lingling An
Order scheduling is of a great significance in the internet and communication industries. With the rapid development of the communication industry and the increasing variety of user demands, the number of work orders for communication operators has grown...
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Herbert Palm and Lorin Arndt
The multi-objective optimization (MOO) of complex systems remains a challenging task in engineering domains. The methodological approach of applying MOO algorithms to simulation-enabled models has established itself as a standard. Despite increasing in c...
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Yu Chen, Qi Dong, Xiaozhou Shang, Zhenyu Wu and Jinyu Wang
Unmanned aerial vehicles (UAVs) are important in reconnaissance missions because of their flexibility and convenience. Vitally, UAVs are capable of autonomous navigation, which means they can be used to plan safe paths to target positions in dangerous su...
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Shaswot Shresthamali, Masaaki Kondo and Hiroshi Nakamura
IoT embedded systems have multiple objectives that need to be maximized simultaneously. These objectives conflict with each other due to limited resources and tradeoffs that need to be made. This requires multi-objective optimization (MOO) and multiple P...
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Yang Sun, Yun Li, Wei Xiong, Zhonghua Yao, Krishna Moniz and Ahmed Zahir
Using Pareto optimization in Multi-Objective Reinforcement Learning (MORL) leads to better learning results for network defense games. This is particularly useful for network security agents, who must often balance several goals when choosing what action...
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Han Jiang, Hongbin Zhang, Zhanyu Feng, Jian Zhang, Yu Qian and Bo Wang
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Tengjie Yang, Lin Zuo, Xinduoji Yang and Nianbo Liu
In recent years, individual learning path planning has become prevalent in online learning systems, while few studies have focused on teaching path planning for traditional classroom teaching. This paper proposes a target-oriented teaching path optimizat...
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Pejman Goudarzi, Mehdi Hosseinpour, Roham Goudarzi and Jaime Lloret
Cloud computing leads to efficient resource allocation for network users. In order to achieve efficient allocation, many research activities have been conducted so far. Some researchers focus on classical optimisation theory techniques (such as multi-obj...
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Yushan Sun, Xiaokun Luo, Xiangrui Ran and Guocheng Zhang
This research aims to solve the safe navigation problem of autonomous underwater vehicles (AUVs) in deep ocean, which is a complex and changeable environment with various mountains. When an AUV reaches the deep sea navigation, it encounters many underwat...
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Jiacun Wang, Guipeng Xi, Xiwang Guo, Shujin Qin and Henry Han
The scheduling of disassembly lines is of great importance to achieve optimized productivity. In this paper, we address the Hybrid Disassembly Line Balancing Problem that combines linear disassembly lines and U-shaped disassembly lines, considering multi...
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Rohit Chowdhury, Atharva Navsalkar and Deepak Subramani
The importance of autonomous marine vehicles is increasing in a wide range of ocean science and engineering applications. Multi-objective optimization, where trade-offs between multiple conflicting objectives are achieved (such as minimizing expected mis...
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Jesús Fernando Cevallos Moreno, Rebecca Sattler, Raúl P. Caulier Cisterna, Lorenzo Ricciardi Celsi, Aminael Sánchez Rodríguez and Massimo Mecella
Video delivery is exploiting 5G networks to enable higher server consolidation and deployment flexibility. Performance optimization is also a key target in such network systems. We present a multi-objective optimization framework for service function cha...
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