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Multiphase Iterative algorithm for Mixed-Integer Optimal Control

JOURNAL OF GUIDANCE CONTROL AND DYNAMICS 2025年第4期48卷 757-770页

作者： Pei, Chaoying You, Sixiong Di, Yu Dai, Ran Purdue Univ W Lafayette IN 47907 USA Missouri Univ Sci & Technol Dept Mech & Aerosp Engn Rolla MO 65409 USA Purdue Univ Sch Aeronaut & Astronaut W Lafayette IN 47907 USA Purdue Univ Dept Stat W Lafayette IN 47907 USA

Mixed-integer optimal control problems (MIOCPs) frequently arise in the domain of optimal control problems (OCPs) when decisions including integer variables are involved. However, existing state-of-the-art approaches for solving MIOCPs are often plagued by drawbacks such as high computational costs, low precision, and compromised optimality. In this study, we propose a novel multiphase scheme coupled with an iterative second-order cone programming (SOCP) algorithm to efficiently and effectively address these challenges in MIOCPs. In the first phase, we relax the discrete decision constraints and account for the terminal state constraints and certain path constraints by introducing them as penalty terms in the objective function. After formulating the problem as a quadratically constrained quadratic programming (QCQP) problem, we propose the iterative SOCP algorithm to solve general QCQPs. In the second phase, we reintroduce the discrete decision constraints to generate the final solution. We substantiate the efficacy of our proposed multiphase scheme and iterative SOCP algorithm through successful application to two practical MIOCPs in planetary exploration missions.

关键词： optimization algorithm Powered Descent Guidance Mixed Integer Optimal Control Quadratically Constrained Quadratic Programming

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Adaptive momentum with discriminative weight for neural network stochastic optimization

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INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS 2022年第9期37卷 6531-6554页

作者： Bai, Jiyang Ren, Yuxiang Zhang, Jiawei Florida State Univ Dept Comp Sci Tallahassee FL 32306 USA Florida State Univ Dept Comp Sci IFM Lab Tallahassee FL 32306 USA Univ Calif Davis IFM Lab Dept Comp Sci Davis CA 95616 USA

optimization algorithms with momentum have been widely used for building deep learning models because of the fast convergence rate. Momentum helps accelerate Stochastic gradient descent in relevant directions in parameter updating, minifying the oscillations of the parameters update route. The gradient of each step in optimization algorithms with momentum is calculated by a part of the training samples, so there exists stochasticity, which may bring errors to parameter updates. In this case, momentum placing the influence of the last step to the current step with a fixed weight is obviously inaccurate, which propagates the error and hinders the correction of the current step. Besides, such a hyperparameter can be extremely hard to tune in applications as well. In this paper, we introduce a novel optimization algorithm, namely, Discriminative wEight on Adaptive Momentum (DEAM). Instead of assigning the momentum term weight with a fixed hyperparameter, DEAM proposes to compute the momentum weight automatically based on the discriminative angle. The momentum term weight will be assigned with an appropriate value that configures momentum in the current step. In this way, DEAM involves fewer hyperparameters. DEAM also contains a novel backtrack term, which restricts redundant updates when the correction of the last step is needed. The backtrack term can effectively adapt the learning rate and achieve the anticipatory update as well. Extensive experiments demonstrate that DEAM can achieve a faster convergence rate than the existing optimization algorithms in training the deep learning models of both convex and nonconvex situations.

关键词： deep learning momentum neural network training optimization algorithm stochastic optimization

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A hybrid framework for day-ahead electricity spot-price forecasting: A case study in China

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APPLIED ENERGY 2024年 373卷

作者： Huang, Siwan Shi, Jianheng Wang, Baoyue An, Na Li, Li Hou, Xuebing Wang, Chunsen Zhang, Xiandong Wang, Kai Li, Huilin Zhang, Sui Zhong, Ming Huaneng Clean Energy Res Inst Bldg AFuture Sci Pk Beijing 102209 Peoples R China China Huaneng Grp Co Ltd 6 Fuxingmennei St Beijing 100031 Peoples R China Huaneng Shandong Power Generat Co Ltd Jinan Shandong Peoples R China Huaneng Jiangxi Power Generat Co Ltd Ganzhou Jiangxi Peoples R China Huaneng Power Int Inc Shanghai Shidongkou Power Plant 1 Shanghai Peoples R China

The electricity price volatility can be aggravated by multiple factors, such as load pattern, line limit, regulations, renewable energy generations, weather conditions and holiday. Due to these complex dynamic characteristics of electricity prices, highly accurate forecasting is quite challenging. Our objective is to provide a hybrid framework to forecast 96-point day-ahead electricity price for the following day. We first conducted a day similarity algorithm (DSA) to construct features from electricity price corresponding to the similar days. A deep neural network (DNN) model was developed from 60 important features, including supply, demand and similar day characteristics, selected by eXtreme Gradient Boosting (XGBoost) algorithm. The hyperparameters were tuned using adaptive TPE (ATPE) method. The framework was validated in the real-world dataset of electricity spot market in Shandong Province in China. The proposed framework had good forecasting performance with the lowest MAE of 0.138, MSE of 0.028, RMSE of 0.166 and U2 of 0.434 in the test set and outperformed the other models significantly. The framework presented a robust methodology for market participants to forecast electricity prices accurately, increase profits and improve decision-making skills.

关键词： Electricity price forecasting Similar day analysis Feature selection optimization algorithm Deep neural networks

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Fruit flexible collecting trajectory planning based on manual skill imitation for grape harvesting robot

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2024年 225卷

作者： Sun, Jiahui Feng, Qingchun Zhang, Yifan Ru, Mengfei Li, Yajun Li, Tao Zhao, Chunjiang Northwest A&F Univ Coll Informat Engn Yangling 712100 Shaanxi Peoples R China Intelligent Equipment Res Ctr Beijing Acad Agr & Forestry Sci Beijing 100097 Peoples R China Beijing Key Lab Intelligent Equipment Technol Agr Beijing 100097 Peoples R China

The flexible collection is vital for the intelligent grape-picking robot. In view of the reliable operation method obtained by long-term training of human prior skill, a fruit flexible collecting trajectory planning method based on manual skill imitation for grape harvesting robot is proposed. The method involves capturing manual teaching trajectory data using a motion capture system, preprocessing the data, extracting features from multiple teaching trajectories, and forming a probability distribution through Gaussian mixture model - Gaussian mixture regression (GMM-GMR). And combined with the key point of manual operation trajectory, the general trajectory generated by GMR is segmented and further imitated by kernelized movement primitives (KMP) to obtain the reference trajectory, respectively. An optimization method for hyperparameter adaptation KMP (O-KMP) was proposed to meet the trajectory fitting effect of multiple key points. Mean square error (MSE) was used to evaluate the deviation of the trajectory from the reference trajectory. The partial optimal trajectory is selected and integrated into a single trajectory. Two experiments were conducted to investigate imitation: For the same starting and ending points task, the MSE of the trajectory generated by O-KMP decreased by 15.274% compared to the original fixed hyperparameter KMP. For different placement tasks, the MSE of the trajectory generated by the O-KMP decreased by 7.296% compared to the original KMP. Finally, the robot arm of the actuator visually presents different task states for the compliant placement of fresh grapes.

关键词： Trajectory planning Manual skill imitation Harvesting robot Kernelized movement primitives optimization algorithm

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Prediction of carbon emissions in China's construction industry using an improved grey prediction model

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SCIENCE OF THE TOTAL ENVIRONMENT 2024年 938卷 173351页

作者： Liu, Jia-Bao Yuan, Xi-Yu Lee, Chien-Chiang Anhui Jianzhu Univ Sch Math & Phys Hefei 230601 Peoples R China Nanchang Univ Sch Econ & Management Nanchang Peoples R China Lebanese Amer Univ Adnan Kassar Sch Business Beirut Lebanon Nanchang Univ Res Ctr Cent China Econ & Social Dev Nanchang Peoples R China

As a significant source of global energy consumption and greenhouse gas emissions, the construction industry garners widespread attention due to its high carbon emissions. Anticipating its development trends is crucial for energy conservation and emission reduction. In this paper, we utilize the carbon emission data from China 's national and provincial construction sectors from 2012 to 2021, employ the grey prediction model optimized by the particle swarm optimization algorithm, coupled with a metabolic algorithm, to forecast the carbon emissions of the construction industry across China and its provinces. The results demonstrate that: (1) The dynamic grey prediction model combined with the metabolism algorithm has a better prediction effect than the classical model, and the relative error is reduced from 5.103 % to 0.874 %. (2) The carbon emissions of China 's construction industry will continue to rise in the next decade, but the growth rate will decrease, and the proportion of indirect carbon emissions continues to increase. (3) There is a marked regional disparity in carbon emissions, with the eastern region exhibiting higher emission levels yet slower growth. In contrast, the western region has lower emission levels but experiences faster growth. These studies provide valuable insights for both the existing approaches to energy conservation and emission reduction, as well as for future policy improvements.

关键词： Construction industry Grey prediction model optimization algorithm Carbon emission Metabolic algorithm

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Dual-Approach Calibration Unlocks Potential of Low-Power, Low-Cost Temperature and Humidity Sensors

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TEHNICKI VJESNIK-TECHNICAL GAZETTE 2024年第4期31卷 1335-1347页

作者： Holik, Mario Barac, Antun Zidar, Josip Stojkov, Marinko Univ Slavonski Brod Mech Engn Fac Slavonski Brod Trg IB Mazuranic 2 Slavonski Brod 35000 Croatia Josip Josip Juraj Strossmayer Univ Osijek Fac Elect Engn Comp Sci & Informat Technol Osijek Kneza Trpimira 2B Osijek 31000 Croatia Univ Slavonski Brod Mech Engn Fac Slavonski Brod Trg IB Mazuranic 2 Slavonski Brod 35000 Croatia

Calibration of low-cost humidity sensors such as the HTS221TR is critical for accurate measurements, especially in smart devices. This study compares two calibration methods: machine learning (PyTorchNeural Network regression model) and optimization algorithm with Engineering Equation Solver. The critical role of temperature in humidity measurement emphasizes that it must be included for a valid calibration. The machine learning approach significantly reduced the average deviation of humidity, reaching +/- 2,5% compared to the original +/- 13,4%. Additionally, it aligned mean values along the identity line. However, the performance of the model varied across the different humidity ranges. Applying the model to real-world scenarios showed that the model underestimates humidity, likely due to the sensor's inherent tendency to overestimate humidity, especially at higher temperatures. Despite these challenges, both calibration methods offer simple and effective approaches for correcting lowcost sensor measurements, with machine learning enabling faster processing. This study not only improves the accuracy of the HTS221TR sensor, but also paves the way for more accurate and affordable humidity measurement technologies in general.

关键词： data processing machine learning optimization algorithm pytorch neural network supply chain monitoring

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A systematic literature review on software security testing using metaheuristics

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AUTOMATED SOFTWARE ENGINEERING 2024年第2期31卷 1-73页

作者： Ahsan, Fatma Anwer, Faisal Aligarh Muslim Univ Dept Comp Sci Aligarh 202002 Uttar Pradesh India

The security of an application is critical for its success, as breaches cause loss for organizations and individuals. Search-based software security testing (SBSST) is the field that utilizes metaheuristics to generate test cases for the software testing for some pre-specified security test adequacy criteria This paper conducts a systematic literature review to compare metaheuristics and fitness functions used in software security testing, exploring their distinctive capabilities and impact on vulnerability detection and code coverage. The aim is to provide insights for fortifying software systems against emerging threats in the rapidly evolving technological landscape. This paper examines how search-based algorithms have been explored in the context of code coverage and software security testing. Moreover, the study highlights different metaheuristics and fitness functions for security testing and code coverage. This paper follows the standard guidelines from Kitchenham to conduct SLR and obtained 122 primary studies related to SBSST after a multi-stage selection process. The papers were from different sources journals, conference proceedings, workshops, summits, and researchers' webpages published between 2001 and 2022. The outcomes demonstrate that the main tackled vulnerabilities using metaheuristics are XSS, SQLI, program crash, and XMLI. The findings have suggested several areas for future research directions, including detecting server-side request forgery and security testing of third-party components. Moreover, new metaheuristics must also need to be explored to detect security vulnerabilities that are still unexplored or explored significantly less. Furthermore, metaheuristics can be combined with machine learning and reinforcement learning techniques for better results. Some metaheuristics can be designed by looking at the complexity of security testing and exploiting more fitness functions related to detecting different vulnerabilities.

关键词： SBSST Meta-heuristic optimization algorithm Evolutionary algorithm Software security testing Code coverage XSS SQLI XMLI Program crash

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Optimum design of planar quasi-Yagi antenna for wearable Internet of Things (IoT) applications

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MICROWAVE AND OPTICAL TECHNOLOGY LETTERS 2024年第1期66卷

作者： Dass, Ruby Thirumoorthy, Gopalakrishnan Ananthakrishnan, Gayathri Kannan, Anusha Rajendran, Sureshbabu Narayanan, Manikandan Arputharaj, Kannan Vellore Inst Technol Sch Comp Sci & Engn Vellore Tamil Nadu India Manipal Acad Higher Educ Manipal Inst Technol Dept Informat Technol Manipal India Vellore Inst Technol Sch Informat Technol & Engn Vellore Tamil Nadu India Vellore Inst Technol Sch Comp Sci & Engn Chennai Tamil Nadu India Kamaraj Coll Engn Dept Elect & Commun Engn Virudunagar India

This paper presents the design of a novel dual-band quasi-Yagi antenna for on-body communications. The proposed antenna is designed on 0.05 mm thin microwave laminate and is designed to cover the 2.45 and 3.5 GHz spectrum to meet the needs of on-body Internet of Things networks. Microstrip to slot-line transition is used for the excitation of the feeder element. The conventional feeder of the quai-Yagi is replaced with F-shaped radiators to obtain a dual-band response. In addition, a pair of directors are used to enhance the antenna gain. The proposed quasi-Yagi antenna is optimized using the spider monkey algorithm. The chosen algorithm is used to synthesize the dimensions of the dual-band radiator and is preferred to obtain quick convergence, unlike the traditional optimization algorithms. The optimized planar quasi antenna offers 5% bandwidth in both operating bands with a measured gain of 5.7 and 5.8 dBi, respectively. The optimization time is also considerably reduced in comparison with traditional optimization algorithms. The simulated and optimized antenna is fabricated and tested.

关键词： dual-band antennas IoT antennas optimization algorithm wearable antennas

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Population diversity and inheritance in genetic programming for symbolic regression

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NATURAL COMPUTING 2024年第3期23卷 531-566页

作者： Burlacu, Bogdan Yang, Kaifeng Affenzeller, Michael Univ Appl Sci Upper Austria HEAL Softwarepk 11 A-4232 Hagenberg Austria

In this work we aim to empirically characterize two important dynamical aspects of GP search: the evolution of diversity and the propagation of inheritance patterns. Diversity is calculated at the genotypic and phenotypic levels using efficient similarity metrics. Inheritance information is obtained via a full genealogical record of evolution as a directed acyclic graph and a set of methods for extracting relevant patterns. Advances in processing power enable our approach to handle previously infeasible graph sizes of millions of arcs and vertices. To enable a more comprehensive analysis we employ three closely-related but different evolutionary models: canonical GP, offspring selection and age-layered population structure. Our analysis reveals that a relatively small number of ancestors are responsible for producing the majority of descendants in later generations, leading to diversity loss. We show empirically across a selection of five benchmark problems that each configuration is characterized by different rates of diversity loss and different inheritance patterns, in support of the idea that each new problem may require a unique approach to solve optimally.

关键词： Genetic programming Symbolic regression Supervised learning Explainable AI optimization algorithm Evolutionary algorithm

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Dynamic capacitated facility location problem in mobile renewable energy charging stations under sustainability consideration

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SUSTAINABLE COMPUTING-INFORMATICS & SYSTEMS 2024年 41卷

作者： Ala, Ali Deveci, Muhammet Bani, Erfan Amani Sadeghi, Amir Hossein Shanghai Jiao Tong Univ Dept Ind Engn & Management Sch Mech Engn Shanghai 200240 Peoples R China Natl Def Univ Turkish Naval Acad Dept Ind Engn TR-34940 Istanbul Turkiye Lebanese Amer Univ Dept Elect & Comp Engn Byblos Lebanon Imperial Coll London Royal Sch Mines South Kensington Campus London SW7 2AZ England Sharif Univ Technol Dept Ind Engn Tehran Iran North Carolina State Univ Dept Ind & Syst Engn Raleigh NC USA

The deployment of mobile renewable energy charging stations plays a crucial role in facilitating the overall adoption of electric vehicles and reducing reliance on fossil fuels. This study addresses the dynamic capacitated facility location problem in mobile charging stations from a sustainability perspective. This paper proposes Twostage stochastic programming with recourse that performs well for this application, and the location of the mobile renewable energy charging station (MRECS) management addresses the complex dynamics of reusable items. To solve this problem, we suggested dealing with differential evolutionary (DE) and DE Q-learning (DEQL) algorithms, as two novel optimization and reinforcement learning approaches, are presented as solution approaches to validate their performance. Evaluation of the outcomes reveals a considerable disparity between the algorithms, and DEQL performs better in solving the presented problem. In addition, DEQL could minimize the total operation cost and carbon emission by 7% and 20%, respectively. In contrast, the DE could decrease carbon emissions and total operation costs by 5% and 2.5%, respectively.

关键词： Renewable energy Stochastic programming optimization algorithm Reinforcement learning Facility location Sustainability

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