Improved Genetic Algorithm Based on Greedy and Simulated Annealing Ideas for Vascular Robot Ordering Strategy

• URL: http://arxiv.org/abs/2403.19484v2
• Date: Tue, 2 Jul 2024 10:20:41 GMT
• Title: Improved Genetic Algorithm Based on Greedy and Simulated Annealing Ideas for Vascular Robot Ordering Strategy
• Authors: Zixi Wang, Yubo Huang, Yukai Zhang, Yifei Sheng, Xin Lai, Peng Lu,
• Abstract summary: This study presents a comprehensive approach for optimizing the acquisition, utilization, and maintenance of ABLVR vascular robots in healthcare settings. Considering the dynamic healthcare environment, our approach includes a robust resource allocation model for robotic vessels and operators. We incorporate the unique requirements of the adaptive learning process for operators and the maintenance needs of robotic components.
• Score: 7.51372615162241
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This study presents a comprehensive approach for optimizing the acquisition, utilization, and maintenance of ABLVR vascular robots in healthcare settings. Medical robotics, particularly in vascular treatments, necessitates precise resource allocation and optimization due to the complex nature of robot and operator maintenance. Traditional heuristic methods, though intuitive, often fail to achieve global optimization. To address these challenges, this research introduces a novel strategy, combining mathematical modeling, a hybrid genetic algorithm, and ARIMA time series forecasting. Considering the dynamic healthcare environment, our approach includes a robust resource allocation model for robotic vessels and operators. We incorporate the unique requirements of the adaptive learning process for operators and the maintenance needs of robotic components. The hybrid genetic algorithm, integrating simulated annealing and greedy approaches, efficiently solves the optimization problem. Additionally, ARIMA time series forecasting predicts the demand for vascular robots, further enhancing the adaptability of our strategy. Experimental results demonstrate the superiority of our approach in terms of optimization, transparency, and convergence speed from other state-of-the-art methods.

Related papers

• Machine Learning Insides OptVerse AI Solver: Design Principles and Applications [74.67495900436728]
  We present a comprehensive study on the integration of machine learning (ML) techniques into Huawei Cloud's OptVerse AI solver. We showcase our methods for generating complex SAT and MILP instances utilizing generative models that mirror multifaceted structures of real-world problem. We detail the incorporation of state-of-the-art parameter tuning algorithms which markedly elevate solver performance.
  arXiv  Detail & Related papers  (2024-01-11T15:02:15Z)
• Efficient Inverse Design Optimization through Multi-fidelity Simulations, Machine Learning, and Search Space Reduction Strategies [0.8646443773218541]
  This paper introduces a methodology designed to augment the inverse design optimization process in scenarios constrained by limited compute. The proposed methodology is analyzed on two distinct engineering inverse design problems: airfoil inverse design and the scalar field reconstruction problem. Notably, this method is adaptable across any inverse design application, facilitating a synergy between a representative low-fidelity ML model, and high-fidelity simulation, and can be seamlessly applied across any variety of population-based optimization algorithms.
  arXiv  Detail & Related papers  (2023-12-06T18:20:46Z)
• RoboGen: Towards Unleashing Infinite Data for Automated Robot Learning via Generative Simulation [68.70755196744533]
  RoboGen is a generative robotic agent that automatically learns diverse robotic skills at scale via generative simulation. Our work attempts to extract the extensive and versatile knowledge embedded in large-scale models and transfer them to the field of robotics.
  arXiv  Detail & Related papers  (2023-11-02T17:59:21Z)
• Contribution \`a l'Optimisation d'un Comportement Collectif pour un Groupe de Robots Autonomes [0.0]
  This thesis studies the domain of collective robotics, and more particularly the optimization problems of multirobot systems. The first contribution is the use of the Butterfly Algorithm Optimization (BOA) to solve the Unknown Area Exploration problem. The second contribution is the development of a new simulation framework for benchmarking dynamic incremental problems in robotics.
  arXiv  Detail & Related papers  (2023-06-10T21:49:08Z)
• End-to-End Meta-Bayesian Optimisation with Transformer Neural Processes [52.818579746354665]
  This paper proposes the first end-to-end differentiable meta-BO framework that generalises neural processes to learn acquisition functions via transformer architectures. We enable this end-to-end framework with reinforcement learning (RL) to tackle the lack of labelled acquisition data.
  arXiv  Detail & Related papers  (2023-05-25T10:58:46Z)
• Representation Learning with Multi-Step Inverse Kinematics: An Efficient and Optimal Approach to Rich-Observation RL [106.82295532402335]
  Existing reinforcement learning algorithms suffer from computational intractability, strong statistical assumptions, and suboptimal sample complexity. We provide the first computationally efficient algorithm that attains rate-optimal sample complexity with respect to the desired accuracy level. Our algorithm, MusIK, combines systematic exploration with representation learning based on multi-step inverse kinematics.
  arXiv  Detail & Related papers  (2023-04-12T14:51:47Z)
• Optimization of Rocker-Bogie Mechanism using Heuristic Approaches [0.0]
  This paper focuses on the Rocker Bogie mechanism, a standard suspension methodology associated with foreign terrains. This paper presents extensive tests on Simulated Annealing, Genetic Algorithms, Swarm Intelligence techniques, Basin Hoping and Differential Evolution.
  arXiv  Detail & Related papers  (2022-09-14T21:02:01Z)
• Resource Planning for Hospitals Under Special Consideration of the COVID-19 Pandemic: Optimization and Sensitivity Analysis [87.31348761201716]
  Crises like the COVID-19 pandemic pose a serious challenge to health-care institutions. BaBSim.Hospital is a tool for capacity planning based on discrete event simulation. We aim to investigate and optimize these parameters to improve BaBSim.Hospital.
  arXiv  Detail & Related papers  (2021-05-16T12:38:35Z)
• Evolutionary Gait Transfer of Multi-Legged Robots in Complex Terrains [14.787379075870383]
  This paper proposes a transfer learning-based evolutionary framework for gait optimization, named Tr-GO. The idea is to initialize a high-quality population by using the technique of transfer learning, so any kind of population-based optimization algorithms can be seamlessly integrated into this framework. The experimental results show the effectiveness of the proposed framework for the gait optimization problem based on three multi-objective evolutionary algorithms.
  arXiv  Detail & Related papers  (2020-12-24T16:41:36Z)
• Bioinspired Bipedal Locomotion Control for Humanoid Robotics Based on EACO [1.0152838128195467]
  This work presents promoting global search capability and convergence rate of the EACO applied to humanoid robots in real-time. We put a special focus on the EACO algorithm on a wide range of problems, from ACO, real-coded GAs, GAs with neural networks(NNs), particle swarm optimization(PSO) to complex robotics systems.
  arXiv  Detail & Related papers  (2020-10-09T09:43:48Z)
• Mixed Strategies for Robust Optimization of Unknown Objectives [93.8672371143881]
  We consider robust optimization problems, where the goal is to optimize an unknown objective function against the worst-case realization of an uncertain parameter. We design a novel sample-efficient algorithm GP-MRO, which sequentially learns about the unknown objective from noisy point evaluations. GP-MRO seeks to discover a robust and randomized mixed strategy, that maximizes the worst-case expected objective value.
  arXiv  Detail & Related papers  (2020-02-28T09:28:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.