Learning to Search Feasible and Infeasible Regions of Routing Problems with Flexible Neural k-Opt

In this paper, we present Neural k-Opt (NeuOpt), a novel learning-to-search (L2S) solver for routing problems. It learns to perform flexible k-opt exchanges based on a tailored action factorization method and a customized recurrent dual-stream decoder. As a pioneering work to circumvent the pure feasibility masking scheme and enable the autonomous exploration of both feasible and infeasible regions, we then propose the Guided Infeasible Region Exploration (GIRE) scheme, which supplements the NeuOpt policy network with feasibility-related features and leverages reward shaping to steer reinforcement learning more effectively. Additionally, we equip NeuOpt with Dynamic Data Augmentation (D2A) for more diverse searches during inference. Extensive experiments on the Traveling Salesman Problem (TSP) and Capacitated Vehicle Routing Problem (CVRP) demonstrate that our NeuOpt not only significantly outstrips existing (masking-based) L2S solvers, but also showcases superiority over the learning-to-construct (L2C) and learning-to-predict (L2P) solvers. Notably, we offer fresh perspectives on how neural solvers can handle VRP constraints. Our code is available: https://github.com/yining043/NeuOpt.Comment: Accepted at NeurIPS 202

Effect of β3-adrenoceptor on cardiac fibrosis in rat cardiac fibroblast cells and its potential mechanism

Purpose: To investigate the effect of β3-adrenoceptors (β3-AR) up-regulation on fibrosis in cardiac fibroblast cells in rats and its potential mechanism.Methods: Cardiac fibroblast cells (CFB) were isolated and identified from rats’ hearts. The β3-ARupregulated cardiac fibroblast cells were constructed by lentiviral transfection technology. Thereafter, Ang II was used to induce fibrosis in cardiac fibroblast cells, and subsequently, Western blot assay was performed to investigate fibrosis related marker proteins (TGF-β, Smad-2, p-Smad-2, Col-I and Col-III) in cardiac fibroblast cells.Results: β3-AR up-regulated cardiac fibroblast cells were successfully constructed. Furthermore, the results show that up-regulation of β3-AR increased the expressions of TGF-β, p-Smad-2, Col-I and Col- III proteins in Ang II treated cardiac fibroblast cells.Conclusion: The results suggest that up-regulation of β3-AR aggravates fibrosis of cardiac fibroblast cells. In other words, inhibition of β3-AR expressions in cardiac tissues would be beneficial for treating cardiac fibrosis and its related cardiac diseases.Keywords: Cardiac fibrosis, β3-AR, TGF/Smads, Col-I/III, Cardiac fibroblast cell

MetaBox: A Benchmark Platform for Meta-Black-Box Optimization with Reinforcement Learning

Recently, Meta-Black-Box Optimization with Reinforcement Learning (MetaBBO-RL) has showcased the power of leveraging RL at the meta-level to mitigate manual fine-tuning of low-level black-box optimizers. However, this field is hindered by the lack of a unified benchmark. To fill this gap, we introduce MetaBox, the first benchmark platform expressly tailored for developing and evaluating MetaBBO-RL methods. MetaBox offers a flexible algorithmic template that allows users to effortlessly implement their unique designs within the platform. Moreover, it provides a broad spectrum of over 300 problem instances, collected from synthetic to realistic scenarios, and an extensive library of 19 baseline methods, including both traditional black-box optimizers and recent MetaBBO-RL methods. Besides, MetaBox introduces three standardized performance metrics, enabling a more thorough assessment of the methods. In a bid to illustrate the utility of MetaBox for facilitating rigorous evaluation and in-depth analysis, we carry out a wide-ranging benchmarking study on existing MetaBBO-RL methods. Our MetaBox is open-source and accessible at: https://github.com/GMC-DRL/MetaBox.Comment: Accepted at NuerIPS 202