Accelerating genetic optimization of nonlinear model predictive control by learning optimal search space size

• URL: http://arxiv.org/abs/2305.08094v1
• Date: Sun, 14 May 2023 08:10:49 GMT
• Title: Accelerating genetic optimization of nonlinear model predictive control by learning optimal search space size
• Authors: Eslam Mostafa, Hussein A. Aly, Ahmed Elliethy
• Abstract summary: This paper proposes an approach to accelerate the optimization of NMPC by learning optimal space size. The proposed approach was compared on two nonlinear systems and compared with two other-based NMPC approaches.
• Score: 0.8057006406834467
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Nonlinear model predictive control (NMPC) solves a multivariate optimization problem to estimate the system's optimal control inputs in each control cycle. Such optimization is made more difficult by several factors, such as nonlinearities inherited in the system, highly coupled inputs, and various constraints related to the system's physical limitations. These factors make the optimization to be non-convex and hard to solve traditionally. Genetic algorithm (GA) is typically used extensively to tackle such optimization in several application domains because it does not involve differential calculation or gradient evaluation in its solution estimation. However, the size of the search space in which the GA searches for the optimal control inputs is crucial for the applicability of the GA with systems that require fast response. This paper proposes an approach to accelerate the genetic optimization of NMPC by learning optimal search space size. The proposed approach trains a multivariate regression model to adaptively predict the best smallest search space in every control cycle. The estimated best smallest size of search space is fed to the GA to allow for searching the optimal control inputs within this search space. The proposed approach not only reduces the GA's computational time but also improves the chance of obtaining the optimal control inputs in each cycle. The proposed approach was evaluated on two nonlinear systems and compared with two other genetic-based NMPC approaches implemented on the GPU of a Nvidia Jetson TX2 embedded platform in a processor-in-the-loop (PIL) fashion. The results show that the proposed approach provides a 39-53\% reduction in computational time. Additionally, it increases the convergence percentage to the optimal control inputs within the cycle's time by 48-56\%, resulting in a significant performance enhancement. The source code is available on GitHub.

Related papers

• Scalable Bayesian Optimization via Focalized Sparse Gaussian Processes [8.40647440727154]
  We argue that Bayesian optimization algorithms with sparse GPs can more efficiently allocate their representational power to relevant regions of the search space. We show that FocalBO can efficiently leverage large amounts of offline and online data to achieve state-of-the-art performance on robot morphology design and to control a 585-dimensional musculoskeletal system.
  arXiv  Detail & Related papers  (2024-12-29T06:36:15Z)
• Optimizing Variational Quantum Circuits Using Metaheuristic Strategies in Reinforcement Learning [2.7504809152812695]
  This work explores the integration of metaheuristic algorithms -- Particle Swarm Optimization, Ant Colony Optimization, Tabu Search, Genetic Algorithm, Simulated Annealing, and Harmony Search -- into Quantum Reinforcement Learning. Evaluations in $5times5$ MiniGrid Reinforcement Learning environments show that, all algorithms yield near-optimal results.
  arXiv  Detail & Related papers  (2024-08-02T11:14:41Z)
• Differentially Private Optimization with Sparse Gradients [60.853074897282625]
  We study differentially private (DP) optimization problems under sparsity of individual gradients. Building on this, we obtain pure- and approximate-DP algorithms with almost optimal rates for convex optimization with sparse gradients.
  arXiv  Detail & Related papers  (2024-04-16T20:01:10Z)
• Frog-Snake prey-predation Relationship Optimization (FSRO) : A novel nature-inspired metaheuristic algorithm for feature selection [0.0]
  This study proposes the Frog-Snake prey-predation Relationship Optimization (FSRO) algorithm. It is inspired by the prey-predation relationship between frogs and snakes for application to discrete optimization problems. The proposed algorithm conducts computational experiments on feature selection using 26 types of machine learning datasets.
  arXiv  Detail & Related papers  (2024-02-13T06:39:15Z)
• Optimal and Near-Optimal Adaptive Vector Quantization [16.628351691108687]
  Quantization is a fundamental optimization for many machine-learning use cases, including compressing, model weights and activations, and datasets. We revisit the Adaptive Vector Quantization (AVQ) problem and present algorithms that find optimal solutions with improved time and space complexity. Our experiments show our algorithms may open the door to using AVQ more extensively in a variety of machine learning applications.
  arXiv  Detail & Related papers  (2024-02-05T16:27:59Z)
• Federated Conditional Stochastic Optimization [110.513884892319]
  Conditional optimization has found in a wide range of machine learning tasks, such as in-variant learning tasks, AUPRC, andAML. This paper proposes algorithms for distributed federated learning.
  arXiv  Detail & Related papers  (2023-10-04T01:47:37Z)
• Fast Computation of Optimal Transport via Entropy-Regularized Extragradient Methods [75.34939761152587]
  Efficient computation of the optimal transport distance between two distributions serves as an algorithm that empowers various applications. This paper develops a scalable first-order optimization-based method that computes optimal transport to within $varepsilon$ additive accuracy.
  arXiv  Detail & Related papers  (2023-01-30T15:46:39Z)
• Genetically Modified Wolf Optimization with Stochastic Gradient Descent for Optimising Deep Neural Networks [0.0]
  This research aims to analyze an alternative approach to optimizing neural network (NN) weights, with the use of population-based metaheuristic algorithms. A hybrid between Grey Wolf (GWO) and Genetic Modified Algorithms (GA) is explored, in conjunction with Gradient Descent (SGD) This algorithm allows for a combination between exploitation and exploration, whilst also tackling the issue of high-dimensionality.
  arXiv  Detail & Related papers  (2023-01-21T13:22:09Z)
• Data-driven evolutionary algorithm for oil reservoir well-placement and control optimization [3.012067935276772]
  Generalized data-driven evolutionary algorithm (GDDE) is proposed to reduce the number of simulation runs on well-placement and control optimization problems. Probabilistic neural network (PNN) is adopted as the classifier to select informative and promising candidates.
  arXiv  Detail & Related papers  (2022-06-07T09:07:49Z)
• High-dimensional Bayesian Optimization Algorithm with Recurrent Neural Network for Disease Control Models in Time Series [1.9371782627708491]
  We propose a new high dimensional Bayesian Optimization algorithm combining Recurrent neural networks. The proposed RNN-BO algorithm can solve the optimal control problems in the lower dimension space. We also discuss the impacts of different numbers of the RNN layers and training epochs on the trade-off between solution quality and related computational efforts.
  arXiv  Detail & Related papers  (2022-01-01T08:40:17Z)
• Neural Predictive Control for the Optimization of Smart Grid Flexibility Schedules [0.0]
  Model predictive control (MPC) is a method to formulate the optimal scheduling problem for grid flexibilities in a mathematical manner. MPC methods promise accurate results for time-constrained grid optimization but they are inherently limited by the calculation time needed for large and complex power system models. A Neural Predictive Control scheme is proposed to learn optimal control policies for linear and nonlinear power systems through imitation.
  arXiv  Detail & Related papers  (2021-08-19T15:12:35Z)
• High dimensional Bayesian Optimization Algorithm for Complex System in Time Series [1.9371782627708491]
  This paper presents a novel high dimensional Bayesian optimization algorithm. Based on the time-dependent or dimension-dependent characteristics of the model, the proposed algorithm can reduce the dimension evenly. To increase the final accuracy of the optimal solution, the proposed algorithm adds a local search based on a series of Adam-based steps at the final stage.
  arXiv  Detail & Related papers  (2021-08-04T21:21:17Z)
• Towards Optimally Efficient Tree Search with Deep Learning [76.64632985696237]
  This paper investigates the classical integer least-squares problem which estimates signals integer from linear models. The problem is NP-hard and often arises in diverse applications such as signal processing, bioinformatics, communications and machine learning. We propose a general hyper-accelerated tree search (HATS) algorithm by employing a deep neural network to estimate the optimal estimation for the underlying simplified memory-bounded A* algorithm.
  arXiv  Detail & Related papers  (2021-01-07T08:00:02Z)
• Adaptive pruning-based optimization of parameterized quantum circuits [62.997667081978825]
  Variisy hybrid quantum-classical algorithms are powerful tools to maximize the use of Noisy Intermediate Scale Quantum devices. We propose a strategy for such ansatze used in variational quantum algorithms, which we call "Efficient Circuit Training" (PECT) Instead of optimizing all of the ansatz parameters at once, PECT launches a sequence of variational algorithms.
  arXiv  Detail & Related papers  (2020-10-01T18:14:11Z)
• Global Optimization of Gaussian processes [52.77024349608834]
  We propose a reduced-space formulation with trained Gaussian processes trained on few data points. The approach also leads to significantly smaller and computationally cheaper sub solver for lower bounding. In total, we reduce time convergence by orders of orders of the proposed method.
  arXiv  Detail & Related papers  (2020-05-21T20:59:11Z)
• Adaptivity of Stochastic Gradient Methods for Nonconvex Optimization [71.03797261151605]
  Adaptivity is an important yet under-studied property in modern optimization theory. Our algorithm is proved to achieve the best-available convergence for non-PL objectives simultaneously while outperforming existing algorithms for PL objectives.
  arXiv  Detail & Related papers  (2020-02-13T05:42:27Z)
• Self-Directed Online Machine Learning for Topology Optimization [58.920693413667216]
  Self-directed Online Learning Optimization integrates Deep Neural Network (DNN) with Finite Element Method (FEM) calculations. Our algorithm was tested by four types of problems including compliance minimization, fluid-structure optimization, heat transfer enhancement and truss optimization. It reduced the computational time by 2 5 orders of magnitude compared with directly using methods, and outperformed all state-of-the-art algorithms tested in our experiments.
  arXiv  Detail & Related papers  (2020-02-04T20:00:28Z)

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.