Zobrazeno 1 - 10
of 290
pro vyhledávání: '"Ma, Yining"'
In this paper we propose MA-DV2F: Multi-Agent Dynamic Velocity Vector Field. It is a framework for simultaneously controlling a group of vehicles in challenging environments. DV2F is generated for each vehicle independently and provides a map of refe
Externí odkaz:
http://arxiv.org/abs/2411.06404
Vehicle Routing Problems (VRPs) can model many real-world scenarios and often involve complex constraints. While recent neural methods excel in constructing solutions based on feasibility masking, they struggle with handling complex constraints, espe
Externí odkaz:
http://arxiv.org/abs/2410.21066
Contemporary research in autonomous driving has demonstrated tremendous potential in emulating the traits of human driving. However, they primarily cater to areas with well built road infrastructure and appropriate traffic management systems. Therefo
Externí odkaz:
http://arxiv.org/abs/2409.05119
Autor:
Goh, Yong Liang, Cao, Zhiguang, Ma, Yining, Dong, Yanfei, Dupty, Mohammed Haroon, Lee, Wee Sun
Existing neural constructive solvers for routing problems have predominantly employed transformer architectures, conceptualizing the route construction as a set-to-sequence learning task. However, their efficacy has primarily been demonstrated on ent
Externí odkaz:
http://arxiv.org/abs/2408.03585
Learning to solve vehicle routing problems (VRPs) has garnered much attention. However, most neural solvers are only structured and trained independently on a specific problem, making them less generic and practical. In this paper, we aim to develop
Externí odkaz:
http://arxiv.org/abs/2405.01029
Evolutionary computation (EC) algorithms, renowned as powerful black-box optimizers, leverage a group of individuals to cooperatively search for the optimum. The exploration-exploitation tradeoff (EET) plays a crucial role in EC, which, however, has
Externí odkaz:
http://arxiv.org/abs/2404.08239
Autor:
Guo, Hongshu, Ma, Yining, Ma, Zeyuan, Chen, Jiacheng, Zhang, Xinglin, Cao, Zhiguang, Zhang, Jun, Gong, Yue-Jiao
Evolutionary algorithms, such as Differential Evolution, excel in solving real-parameter optimization challenges. However, the effectiveness of a single algorithm varies across different problem instances, necessitating considerable efforts in algori
Externí odkaz:
http://arxiv.org/abs/2403.02131
Autor:
Ma, Zeyuan, Guo, Hongshu, Chen, Jiacheng, Peng, Guojun, Cao, Zhiguang, Ma, Yining, Gong, Yue-Jiao
Recent research explores optimization using large language models (LLMs) by either iteratively seeking next-step solutions from LLMs or directly prompting LLMs for an optimizer. However, these approaches exhibit inherent limitations, including low op
Externí odkaz:
http://arxiv.org/abs/2403.01131
Autor:
Du, Yingpeng, Wang, Ziyan, Sun, Zhu, Chua, Haoyan, Liu, Hongzhi, Wu, Zhonghai, Ma, Yining, Zhang, Jie, Sun, Youchen
In recent years, efforts have been made to use text information for better user profiling and item characterization in recommendations. However, text information can sometimes be of low quality, hindering its effectiveness for real-world applications
Externí odkaz:
http://arxiv.org/abs/2402.08859
Recent Meta-learning for Black-Box Optimization (MetaBBO) methods harness neural networks to meta-learn configurations of traditional black-box optimizers. Despite their success, they are inevitably restricted by the limitations of predefined hand-cr
Externí odkaz:
http://arxiv.org/abs/2402.02355