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Early Termination of Convex QP Solvers in Mixed-Integer Programming for Real-Time Decision Making

IEEE CONTROL SYSTEMS LETTERS 2021年第4期5卷 1417-1422页

作者： Liang, Jiaming Di Cairano, Stefano Quirynen, Rien Mitsubishi Elect Res Labs Cambridge MA 02139 USA Georgia Inst Technol Sch Ind & Syst Engn Atlanta GA 30332 USA

The branch-and-bound optimization algorithm for mixed-integer model predictive control (MI-MPC) solves several convex quadratic program relaxations, but often the solutions are discarded based on already known integer feasible solutions. This letter presents a projection and early termination strategy for infeasible interior point methods to reduce the computational effort of finding a globally optimal solution for MI-MPC. The method is shown to be also effective for infeasibility detection of the convex relaxations. We present numerical simulation results with a reduction of the total number of solver iterations by 42% for an MI-MPC example of decision making for automated driving with obstacle avoidance constraints.

关键词： Cyber-physical systems mixed-integer programming optimization algorithms

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Post-earthquake recover strategy for substations based on seismic resilience evaluation

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ENGINEERING STRUCTURES 2023年第1期279卷

作者： Liu, Xiao Xie, Qiang Liang, Huangbin Zhang, Xiuli Tongji Univ Coll Civil Engn Shanghai 200092 Peoples R China South China Agr Univ Coll Water Conservancy & Civil Engn Guangzhou 510640 Peoples R China

Electrical substations need a sufficient amount of time to repair damaged equipment and restore power after an earthquake. Yet, the devastation and danger inherent in an earthquake requires fastest return to power possible;thus, finding better methods to improve the efficiency of post-earthquake emergency recover is an urgent issue. This paper presents a rapid seismic resilience assessment framework which combines a network model and functional time-varying feature. The authors developed a dual-dimensional functional network model of a typical 220 kV substation built with an emphasis on its connectivity capabilities and the power transmission capacity of its equipment. The model's rapid function status was evaluated based on its network dependence on Bayesian network nodes. The authors' post-earthquake iterative analysis focuses on resource constraint and power user importance. This article shows how the authors obtained the stepped functional time-varying function as a basis for quantification in the post-earthquake recovery process and provides a seismic resilience analysis of the electrical substation. The multi-objective heuristic optimization algorithm was developed to determine an optimal post-earthquake multi-level repair strategy for substation post-earthquake recovery and to determine substation's seismic resilience levels.

关键词： Substation system Functional state model Seismic resilience Rapid assessment method optimization algorithms Recover strategies

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AdaDerivative optimizer: Adapting step-sizes by the derivative term in past gradient information

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2023年第1期119卷

作者： Zou, Weidong Xia, Yuanqing Cao, Weipeng Beijing Inst Technol Sch Automation Beijing 100081 Peoples R China Guangdong Lab Artificial Intelligence & Digital Ec Shenzhen 518107 Peoples R China

AdaBelief fully utilizes "belief'' to iteratively update the parameters of deep neural networks. However, the reliability of the "belief'' is determined by the gradient's prediction accuracy, and the key to this prediction accuracy is the selection of the smoothing parameter beta(1). AdaBelief also suffers from the overshoot problem, which occurs when the value of parameters exceeds the value of the target and cannot be changed along the gradient direction. In this paper, we propose AdaDerivative to eliminate the overshoot problem of AdaBelief. The key to AdaDerivative is that the "belief'' of AdaBelief is replaced by the derivative term's exponential moving average (EMA), which can be constructed as (1 -beta(2)) Sigma(i)(i =1) beta(t-t)(2) (g(t) - g(t-1))(2) based on the past and current gradients. We validate the performance of AdaDerivative on a variety of tasks, including image classification, language modeling, node classification, image generation, and object detection tasks. Extensive experimental results demonstrate that AdaDerivative can achieve state-of-the-art performance.

关键词： Deep neural networks optimization algorithms Adam Stochastic gradient descent

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New automatic (IDS) in IoTs with artificial intelligence technique

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OPTIK 2023年第1期273卷

作者： Jasim, Alaa Firas Jasim Kurnaz, Sefer Altinbas Univ Elect & Comp Engn Istanbul Turkey

In this study, we will be developed new framework-based deep learning techniques for IDS detection in a wireless sensor network. This study uses three methods for developing Network Intrusion Detection systems in IoTs. In the first method, we try to train the deep learning tech-nique using an optimization algorithm. The aim of using optimization algorithm BBO and other algorithms to develop IoTs security system. In the second method, an optimization algorithm is applied as a feature selection method and combined with a classifier to develop a new Network Intrusion Detection system. Finally, CNN Convolution Neural Network LSTM (Long Short-Term Memory layer) was applied with an "Adam "optimizer to train and evaluate data and to read and drop any invalid data from the dataset.

关键词： IoTs Sparse Auto Encoder CNN LSTM Ensemble Learning optimization algorithms Intrusion Detection System (IDS)

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Revolutionizing sustainable supply chain management: A review of metaheuristics

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2023年第PartA期126卷

作者： Abualigah, Laith Hanandeh, Essam Said Abu Zitar, Raed Thanh, Cuong-Le Khatir, Samir Gandomi, Amir H. Al al Bayt Univ Prince Hussein Bin Abdullah Fac Informat Technol Comp Sci Dept Mafraq 25113 Jordan Ho Chi Minh City Open Univ Ctr Engn Applicat & Technol Solut Ho Chi Minh 700000 Vietnam Zarqa Univ Dept Comp Informat Syst Zarqa 13132 Jordan Sorbonne Univ Abu Dhabi Sorbonne Ctr Artificial Intelligence Abu Dhabi U Arab Emirates Univ Ghent Fac Engn & Architecture Dept Elect Energy Lab Soete B-9000 Ghent Belgium Univ Technol Sydney Fac Engn & Informat Technol Ultimo NSW 2007 Australia Obuda Univ Univ Res & Innovat Ctr EKIK H-1034 Budapest Hungary

This paper reviews the application of metaheuristics for optimized sustainable supply chain management (SSCM). This paper explores the potential of metaheuristics to improve the supply chain's sustainability while enhancing its efficiency and competitiveness. The paper provides an overview of the principles of SSCM and the challenges businesses face in achieving sustainable supply chain management. It then introduces the concept of metaheuristics and describes their use in solving complex optimization problems. The paper reviews various metaheuristics algorithms applied to sustainable supply chain management and analyzes their effectiveness in addressing the challenges of SSCM. The paper also identifies the key factors that influence the success of using metaheuristics for SSCM, such as the choice of algorithm, problem complexity, and data quality. Finally, the paper provides recommendations for future research in this area and highlights the potential of metaheuristics to promote sustainable supply chain management. The review suggests that metaheuristics can be a valuable tool for optimizing sustainable supply chain management and improving supply chain operations' sustainability, efficiency, and competitiveness.

关键词： Meta -heuristics optimization algorithms Sustainable Supply chain management

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Gradient Methods With Dynamic Inexact Oracles

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IEEE CONTROL SYSTEMS LETTERS 2021年第4期5卷 1163-1168页

作者： Han, Shuo Univ Illinois Dept Elect & Comp Engn Chicago IL 60607 USA

We present a framework for generalizing the primal-dual gradient method, also known as the gradient descent ascent method, for solving convex-concave minimax problems. The framework is based on the observation that the primal-dual gradient method can be viewed as an inexact gradient method applied to the primal problem. Unlike the setting of traditional inexact gradient methods, the inexact gradient is computed by a dynamic inexact oracle, which is a discrete-time dynamical system whose output asymptotically approaches the exact gradient. For minimax problems, dynamic inexact oracles are capable of modeling a range of first-order methods for computing the gradient of the primal objective, which relies on solving the inner maximization problem. We provide a unified convergence analysis of gradient methods with dynamic inexact oracles and demonstrate its use in creating new accelerated primal-dual algorithms.

关键词： Gradient methods Heuristic algorithms Radio frequency Convergence Approximation algorithms Acceleration Handheld computers optimization algorithms stability of nonlinear systems

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A Review on Software Refactoring Opportunity Identification and Sequencing in Object-oriented Software

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RECENT ADVANCES IN ELECTRICAL & ELECTRONIC ENGINEERING 2021年第3期14卷 252-267页

作者： Kaur, Satnam Awasthi, Lalit K. Sangal, Amrit L. Dr BR Ambedkar Natl Inst Technol Dept Comp Sci & Engn Jalandhar Punjab India

Background: Owing to the benefits of software refactoring, the software industry started adopting this practice in the maintenance phase as a means to improve developer's productivity and software quality. As a result, proposing new techniques for refactoring opportunity identification and sequencing has become the key area of interest for academicians and industry researchers. Objective: This paper aims to perform a review of such existing approaches which are related to software refactoring opportunity identification and sequencing. Methods: We discussed the background concepts of code smells and refactoring and provided their corresponding taxonomies. Moreover, comprehensive literature of several techniques that automatically or semi-automatically identify or prioritize the refactoring opportunities is presented along with considered refactoring activities, optimization algorithms, bad smells, datasets and underlying evaluation approaches. Results: The research in the direction of refactoring opportunity identification and sequencing is highly active and is generally performed by academic researchers. Most of the techniques address Move Method and Extract Class refactoring activities in Java datasets. Conclusion: This paper highlights various open challenges that need further investigation, including lack of dynamic analysis-based approaches, lesser utilization of industrial datasets, nonconsideration of recent optimization algorithms, etc.

关键词： Software refactoring refactoring opportunity identification refactoring sequencing software quality optimization algorithms metaheuristic approaches

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New Fuzzy-Heuristic Methodology for Analyzing Compression Load Capacity of Composite Columns

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BUILDINGS 2023年第1期13卷 125-125页

作者： Karimi Sharafshadeh, Bizhan Ketabdari, Mohammad Javad Azarsina, Farhood Amiri, Mohammad Nehdi, Moncef L. Islamic Azad Univ Dept Civil Engn Qeshm Branch Qeshm 795151393 Iran Amirkabir Univ Technol Dept Maritime Engn Tehran *** Iran Islamic Azad Univ Dept Marine Struct Sci & Res Branch Tehran 14515775 Iran Univ Hormozgan Fac Engn Bandar Abbas *** Iran McMaster Univ Dept Civil Engn Hamilton ON L8S 4L8 Canada

Predicting the mechanical strength of structural elements is a crucial task for the efficient design of buildings. Considering the shortcomings of experimental and empirical approaches, there is growing interest in using artificial intelligence techniques to develop data-driven tools for this purpose. In this research, empowered machine learning was employed to analyze the axial compression capacity (CC) of circular concrete-filled steel tube (CCFST) composite columns. Accordingly, the adaptive neuro-fuzzy inference system (ANFIS) was trained using four metaheuristic techniques, namely earthworm algorithm (EWA), particle swarm optimization (PSO), salp swarm algorithm (SSA), and teaching learning-based optimization (TLBO). The models were first applied to capture the relationship between the CC and column characteristics. Subsequently, they were requested to predict the CC for new column conditions. According to the results of both phases, all four models could achieve dependable accuracy. However, the PSO-ANFIS was tangibly more efficient than the other models in terms of computational time and accuracy and could attain more accurate predictions for extreme conditions. This model could predict the CC with a relative error below 2% and a correlation exceeding 99%. The PSO-ANFIS is therefore recommended as an effective tool for practical applications in analyzing the behavior of the CCFST columns.

关键词： composite elements CCFSTC columns compression capacity machine learning optimization algorithms

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Intelligent Ship Collision Avoidance Support System Based on the Algorithm of Anthropomorphic Physics

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International Journal of Ambient Computing and Intelligence 2024年第1期15卷

作者： Feng, Guoxu Gu, Songbo Sun, Shihu Nanjing University of Aeronautics and Astronautics China Hebei Jiaotong Vocational and Technical College China

Most of the collision-related decisions of ships at sea depend on the working experience of drivers and determining a reasonable avoidance decision quickly when facing a multivessel encounter situation is difficult, so applying intelligent algorithms to assist these decisions is necessary. On the basis of this, the authors researched the construction of intelligent decision support systems for ship collision avoidance that relies on an anthropomorphic physics optimization algorithm. They used this algorithm to obtain the global range optimal solutions through iteration, which provides effective decisions for ship collision avoidance. The experiments were designed to simulate and analyze the ship collision avoidance decision model. The results showed that the decision-making system based on the anthropomorphic physics optimization algorithm can provide an effective collision avoidance decision scheme. © 2024 IGI Global. All rights reserved.

关键词： optimization algorithms

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Hyperparameter Search for Machine Learning algorithms for Optimizing the Computational Complexity

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PROCESSES 2023年第2期11卷 349页

作者： Ali, Yasser A. Awwad, Emad Mahrous Al-Razgan, Muna Maarouf, Ali King Saud Univ Dept Informat Syst Coll Comp & Informat Sci POB 51178 Riyadh 11543 Saudi Arabia King Saud Univ Coll Engn Elect Engn Dept POB 800 Riyadh 11421 Saudi Arabia King Saud Univ Dept Software Engn Coll Comp & Informat Sci POB 22452 Riyadh 11495 Saudi Arabia

For machine learning algorithms, fine-tuning hyperparameters is a computational challenge due to the large size of the problem space. An efficient strategy for adjusting hyperparameters can be established with the use of the greedy search and Swarm intelligence algorithms. The Random Search and Grid Search optimization techniques show promise and efficiency for this task. The small population of solutions used at the outset, and the costly goal functions used by these searches, can lead to slow convergence or execution time in some cases. In this research, we propose using the machine learning model known as Support Vector Machine and optimizing it using four distinct algorithms-the Ant Bee Colony Algorithm, the Genetic Algorithm, the Whale optimization, and the Particle Swarm optimization-to evaluate the computational cost of SVM after hyper-tuning. Computational complexity comparisons of these optimization algorithms were performed to determine the most effective strategies for hyperparameter tuning. It was found that the Genetic Algorithm had a lower temporal complexity than other algorithms.

关键词： hyperparameter tuning machine learning optimization algorithms ant bee colony (ABC) genetic algorithm (GA) whale optimization (WO) particle swarm optimization (PSO) support vector machine (SVM)

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