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Meta-optimization for improved efficiency: an integrated methodology

International Journal of Information Technology (Singapore) 2025年第4期17卷 2375-2386页

作者： Singha, Anjani Kumar Yadav, Gaurav Hasanpuri, Vijay Patel, Rajiv Ranjan Department of Computer Applications SRM Institute of Science and Technology (‘Deemed-to-Be-University’) Delhi-NCR Campus Ghaziabad 201204 India Department of Computer Science Aligarh Muslim University Aligarh India UIET Kurukshetra University Kurukshetra India Haridwar University Roorkee India

The use of optimization algorithms is essential to train neural networks effectively. The usage of a combination of two different optimizers is proposed in this method that, used together, can perform single optimizers in various problems. The objective of this study is to develop a comprehensive framework that combines meta-optimization techniques with traditional optimization methods to enhance performance metrics such as convergence speed, solution quality, and robustness against local minima. The researcher presents ATMO (Adaptive Meta Optimizers), recent deep neural network (DNN) models that are combined with a new optimizer that combines two different optimizers simultaneously. Meta-optimizer considers all of their contributions at once, rather than trying to enhance each one separately. Using a wide range of techniques, DNNs have been the subject of numerous investigations on text and image categorization. Results from these tests demonstrated that the suggested Adaptive Meta Optimizer outperforms individual optimizers. These experiments show that adequate training is achieved by considering a variety of factors such as Keras standard learning rate and convergence, and degradation on the CIFAR-100, CIFAR-10, and MNIST datasets, and the results are obtained using Python. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2025.

关键词： AdapTiveMeta Optimizers-ATMO Deep learning Image classification optimization algorithm Optimizers Text classification

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A Bi-fidelity Strategy for optimization under Uncertainty: with Applications to Aircraft Trajectory optimization

A Bi-fidelity Strategy for Optimization under Uncertainty: w...

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AIAA SciTech Forum

作者： Dixon, Thomas O. Gorodetsky, Alex A. Univ Michigan Dept Aerosp Engn Ann Arbor MI 48104 USA

ISBN: (数字)9781624107115

ISBN: (纸本)9781624107115

We test a simple bi-fidelity strategy for accelerating trajectory optimization under uncertainty in the presence of robust constraints. Our approach combines the accuracy of high-fidelity models with the computational efficiency of low-fidelity counterparts to increase the accuracy of constraint computation for a given cost. Specifically, we accelerate sample average approaches that use high-fidelity models, with additional samples of low-fidelity models for assessing whether or not a risk constraint is violated. The low-fidelity model's purpose is thus to reduce the uncertainty in the estimate of constraint violation, at the expense of introduction of bias. Our empirical test on a simulation of glider dynamics tasked with avoiding a region indicates an order-of-magnitude less failures using the bi-fidelity strategy.

关键词： Optimal Control Problem Sample Average Approximation optimization algorithm Collocation Method Quantiles Runge Kutta Integration Boundary Value Problems Autonomous Systems Space Exploration Python

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Landing optimization on the Special Euclidean Group SE(3) Under Control and State-Triggered Inequality Constraints

Landing Optimization on the Special Euclidean Group SE(3) Un...

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AIAA SciTech Forum

作者： McCann, Brennan S. Nazari, Morad Embry Riddle Aeronaut Univ Aerosp Engn 1 Aerosp Blvd Daytona Beach FL 32114 USA

ISBN: (数字)9781624107115

ISBN: (纸本)9781624107115

This paper studies rigid body trajectory optimization for spacecraft landing. Formulating the rigid-body dynamics on the special Euclidean group (SE(3)) mitigates singularity and nonuniquness issues of attitude parameterization sets. To perform the optimization problem using these dynamics, classical methods for optimization are generalized to Riemannian manifolds and trajectory optimization problems. Riemannian on-manifold optimization methods are used to provide an algorithm for optimizing the spacecraft landing problem on SE(3). In addition, inequality constraints and control input limits are applied, and the problem is solved using a Riemannian augmented Lagrangian method with trapezoidal collocation.

关键词： Optimal Control Problem Augmented Lagrangian Method optimization algorithm Spacecraft Landing Rigid Body Dynamics Lagrange Multipliers Taylor Series Linear Interpolation Heuristic Approach Search algorithm

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Research on Radar Operation Pattern Recognition Method Based on MLP optimization 24

Research on Radar Operation Pattern Recognition Method Based...

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3rd International Conference on Cryptography, Network Security and Communication Technology (CNSCT)

作者： Li, Hao Zhang, Fuqun Feng, Mingyue Zhang, Jiapeng Huang, Linxuan Tian, Tian Air Force Early Warning Acad Wuhan Peoples R China Xidian Univ Xian Peoples R China PLA Unit 93277 Shenyang Peoples R China

ISBN: (纸本)9798400716959

This article delves into an innovative radar working pattern recognition algorithm based on multi-layer perceptron (MLP). Through carefully designed optimization algorithms, we systematically searched and determined the optimal MLP network structure to solve the radar operating pattern recognition problem. In a detailed simulation experiment, we carefully analyzed the effects of various network parameters, including the number of network layers, number of neurons, learning rate, and batch rate. The experimental results show that the MLP network can exhibit optimal performance when the number of layers is 5, the number of neurons is 512, the learning rate is 0.006, and the batch rate is 10. This discovery provides us with a highly promising solution to the problem of radar working pattern recognition.

关键词： Multifunctional radar Radar working mode recognition neural network optimization algorithm multi-layer perceptron

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SlabCutOpt: A code for ornamental stone slab cut optimization

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SOFTWARE IMPACTS 2024年 22卷

作者： Bondua, S. Focaccia, S. Elkarmoty, M. Univ Bologna DICAM Via Terracini 28 I-40131 Bologna Italy Cairo Univ Fac Engn Dept Min Petr & Met Engn 1 Gamaa St Giza 12613 Egypt

Rock masses are naturally affected by discontinuities, joints and fractures that affect their exploitation. After block extraction, different cutting pattern can produce different recovery ratio of the block. The optimization of the cutting pattern can be computed if the discontinuities are mapped by the use of non-destructive methods. We propose a software code able to compute the number of intersected slabs by different cuttings scenarios. The algorithm adopts a brute force computation of several scenarios as specified by the user. The software uses the Open MP library in order to reduce computation time.

关键词： Slabs optimization algorithm Rock Ornamental stone Triangle intersection

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Starling murmuration optimizer: A novel bio-inspired algorithm for global and engineering optimization

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COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 2022年 392卷 114616-114616页

作者： Zamani, Hoda Nadimi-Shahraki, Mohammad H. Gandomi, Amir H. Islamic Azad Univ Fac Comp Engn Najafabad Branch Najafabad Iran Islamic Azad Univ Big Data Res Ctr Najafabad Branch Najafabad Iran Univ Technol Sydney Fac Engn & Informat Technol Ultimo Australia

This paper presents a novel bio-inspired algorithm inspired by starlings' behaviors during their stunning murmuration named starling murmuration optimizer (SMO) to solve complex and engineering optimization problems as the most appropriate application of metaheuristic algorithms. The SMO introduces a dynamic multi-flock construction and three new search strategies, separating, diving, and whirling. The separating search strategy aims to enhance the population diversity and local optima avoidance using a new separating operator based on the quantum harmonic oscillator. The diving search strategy aims to explore the search space sufficiently by a new quantum random dive operator, whereas the whirling search strategy exploits the vicinity of promising regions using a new operator called cohesion force. The SMO strikes a balance between exploration and exploitation by selecting either a diving strategy or a whirling strategy based on the flocks' quality. The SMO was tested using various benchmark functions with dimensions 30, 50, 100. The experimental results prove that the SMO is more competitive than other state-of-the-art algorithms regarding solution quality and convergence rate. Then, the SMO is applied to solve several mechanical engineering problems in which results demonstrate that it can provide more accurate solutions. A statistical analysis shows that SMO is superior to the other contenders. (c) 2022 Elsevier B.V. All rights reserved.

关键词： optimization algorithm Metaheuristic algorithm Bio-inspired algorithm Swarm intelligence algorithm Quantum computing Applied mechanics and engineering problems

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Evaluation of the Efficiency of the optimization algorithms for Transfer Learning on the Rice Leaf Disease Dataset

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2022年第10期13卷 83-91页

作者： Quach, Luyl-Da Quoc, Khang Nguyen Quynh, Anh Nguyen Ngoc, Hoang Tran FPT Univ Cantho City Vietnam

To improve the model's efficiency, people use many different methods, including the Transfer Learning algorithm, to improve the efficiency of recognition and classification of image data. The study was carried out to combine optimization algorithms with the Transfer Learning model with MobileNet, MobileNetV2, InceptionV3, Xception, ResNet50V2, DenseNet201 models. Then, testing on rice disease data set with 13.186 images, background removed. The result obtained with high accuracy is the RMSprop algorithm, with an accuracy of 88% when combined with the Xception model, similar to the F1, Xception model, and ResNet50V2 score of 87% when combined with the Adam algorithm. This shows the effect of gradients on the Transition learning model. Research, evaluate and draw the optimal model to build a website to identify diseases on rice leaves, with the main functions including images and recording of disease identification points for better management purposes on diseased areas of rice.

关键词： optimization algorithm transfer learning RMSprop rice leaf disease Adam

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Adjoint optimization of the BGK Equation with an Embedded Neural Network for Reduced-Order Modeling of Hypersonic Flows

Adjoint Optimization of the BGK Equation with an Embedded Ne...

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AIAA SciTech Forum

作者： Ball, Nicholas Daultry Panesi, Marco MacArt, Jonathan F. Sirignano, Justin Univ Oxford Math Inst Oxford England Univ Illinois Aerosp Engn Champaign IL USA Univ Notre Dame Aerosp & Mech Engn Fremantle WA 6959 Australia

ISBN: (数字)9781624107115

ISBN: (纸本)9781624107115

Numerical simulation of the Boltzmann equation, most commonly by the DSMC method, can provide highly accurate descriptions of supersonic, rarefied flows. However, it is computationally expensive in the transitional flow regime, with Knudsen numbers 0.01 less than or similar to Kn less than or similar to 0.1, motivating the use of simpler, but less accurate models such as the BGK equation. We develop and implement optimization methods for calibrating machine learning-based BGK models to high-fidelity DSMC data. We derive and validate an adjoint equation for optimization over the BGK equation with a general form of the collision frequency. We train a neural network model for the collision frequency using automatic differentiation to implement the adjoint equations, and apply this model to the formation of a 1D shock.

关键词： Compressible Flow Neural Networks Reduced Order Modelling Direct Simulation Monte Carlo Direct Numerical Simulation optimization algorithm Conservation of Mass Boltzmann Equation Machine Learning Mach Numbers

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Deep Reinforcement Learning-Based Energy-Conscious Scheduling Under Time-of-Use Electricity Price

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 11490-11504页

作者： Wang, Caixia Fang, Wenxuan Dai, Xin He, Renchu Du, Wei Tang, Yang East China Univ Sci & Technol State Key Lab Ind Control Technol Minist Educ Shanghai 200237 Peoples R China East China Univ Sci & Technol Key Lab Smart Mfg Energy Chem Proc Minist Educ Shanghai 200237 Peoples R China Huzhou Inst Ind Control Technol Huzhou 313099 Peoples R China Westlake Univ Trustworthy & Gen Artificial Intelligence Lab Hangzhou 310030 Zhejiang Peoples R China China Univ Petr Coll Artificial Intelligence Sch Dept Automat Beijing 102249 Peoples R China

In the context of carbon peaking and carbon neutrality, green production scheduling that considers energy has attracted increasing attention. Reinforcement learning (RL) has emerged as a topic for developing efficient algorithms for solving complex combinatorial optimization problems, such as real-world shop scheduling problems. In this paper, we investigate the energy-conscious scheduling problem (ECSP) under time-of-use (TOU) electricity price with the goal of minimizing both the waiting time and extra electricity cost. A mathematical model is formulated, and an efficient deep RL (DRL)-based optimization method is proposed to solve the problem effectively. We design a novel ECSP network (ECSPNet) tailored to handle various ECSP scales based on the characteristics of the problem. Moreover, the Tchebycheff decomposition method is used to solve the multi-objective optimization problems, complemented by the application of the policy gradient method from reinforcement learning to train the ECSPNet without size limitations. Experiments verify that the proposed ECSPNet outperforms state-of-the-art methods and is computationally efficient, even on instances of larger scales unseen in training. Real-world case studies reveal that the proposed method can reduce annual total electricity cost by approximately 30% while effectively maximizing production efficiency.

关键词： Electricity Costs Production Job shop scheduling Automation Energy consumption Training Conferences Power demand Decision making Deep reinforcement learning energy-conscious scheduling problem time of use electricity price optimization algorithm

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A hybrid machine learning model for NOx emission concentration prediction from sludge incineration

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PROCESS SAFETY AND ENVIRONMENTAL PROTECTION 2025年 196卷

作者： Luo, Song Wang, Lihua Ji, Hongxian Zhong, Qifeng Cui, Haibin Wang, Fei Zhejiang Univ State Key Lab Clean Energy Utilizat Hangzhou 310027 Peoples R China Shanghai Chengtou Water Grp Co Ltd Shanghai 201709 Peoples R China

Accurate prediction of NOx emission concentration is crucial for optimizing combustion processes and enhancing flue gas treatment in incineration systems. However, the traditional prediction models that employ data-driven methods face significant challenges due to insufficient input feature information, as well as low computational efficiency and robustness. These limitations hinder the accurate real-time prediction of NOx emission concentration. To address this issue, this paper proposes an advanced hybrid model for predicting NOx emission concentration. Initially, static and dynamic flame features are extracted from flame images and integrated with Distributed Control System (DCS) parameters to serve as the model's input features, while the NOx emission concentration constitutes the model's output feature. Subsequently, the lag time between NOx emissions and the input features is determined using mutual information (MI), followed by data reorganization to develop various predictive models for NOx emission concentration. Finally, the extremely randomized trees (ERT) model, demonstrating superior performance, is further optimized using Bayesian optimization with tree-structured Parzen estimators (BO-TPE). Experimental results indicate that the ERT model optimized with BO-TPE outperforms state-of-the-art models, making it suitable for online optimization of industrial pollutant control and potentially contributing to cleaner production.

关键词： NO x emission concentration prediction Sludge incineration Flame image Extremely randomized trees optimization algorithm

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