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Solving Multi-Objective Optimal Control Problems Using a Multiresolution Approach

JOURNAL OF GUIDANCE CONTROL AND DYNAMICS 2025年第1期48卷 32-45页

作者： Parsonage, Ben Maddock, Christie Univ Strathclyde Dept Mech & Aerosp Engn Glasgow Scotland

This paper presents an adaptive multiresolution strategy for multi-objective optimal control problems. The optimal control problem is solved using a direct approach, with individualistic grid adaptation facilitated by a local error analysis at element boundaries. Multiple objectives are considered using a dominance-based approach applying both local and global search methods to a collaborative population of unique solutions. These aspects are simultaneously incorporated via a novel application of evolutionary algorithms for adaptive optimal control problems. Together, this avoids the need for a priori specification of the quantity and temporal location of element boundaries and the set of scalarization weights defining the multi-objective descent directions. Solution fidelity can thus increase concurrently with the exploration of the design space, which leads to increased numerical efficiency while propagating and maintaining population diversity. The benefits of the proposed approach over traditional uniform-grid implementations are demonstrated. Results show that the multiresolution approach is capable of striking an effective balance between solution fidelity, population diversity, and computational cost unachievable using uniform grids.

关键词： Evolutionary algorithm Adaptive Mesh Refinement Multidisciplinary Design and optimization optimization algorithm Genetic algorithm Multi-Objective Evolutionary algorithms Optimal Control Problem

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Utilizing tuned mass damper for reduction of seismic pounding between two adjacent buildings with different dynamic characteristics

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SOIL DYNAMICS AND EARTHQUAKE ENGINEERING 2025年 188卷

作者： Kamgar, Reza Dadkhah, Masoud Heidarzadeh, Heisam Javanmardi, Mahmoud Seidali Shahrekord Univ Fac Engn Civil Engn Dept Shahrekord Iran Shahid Bahonar Univ Kerman Civil Engn Dept Kerman Iran

Today, population growth and the need for constructing adjacent buildings have raised the likelihood of pounding between adjacent buildings with different dynamic characteristics. The pounding force applied to adjacent buildings during an earthquake is intensive and may cause total or partial damage to structural elements, leading to collapse. It disturbs and complicates the functioning of buildings during and after an earthquake. Therefore, the main aim of this paper is to minimize and, if possible, eliminate the pounding force between two adjacent structures by considering three objective functions. For this aim, the tuned mass damper is utilized here. Also, this paper aims to reduce the number of collisions between different stories of adjacent buildings through a tuned mass damper system. For this purpose, two adjacent steel moment frames with six and ten stories are nonlinearly modeled and validated using the concentrated plasticity model in OpenSees. Moreover, the pounding element is nonlinearly modeled and validated. Then, tuned mass dampers (TMDs) are used on the roofs of the structures. They are optimized in stiffness, mass, and damping coefficient using the grey wolf optimization (GWO) algorithm. Nonlinear time history analysis (NLTHA) is performed under nine far- and near- field earthquakes. The pounding force and structural responses are analyzed, including maximum story acceleration, maximum inter-story drift, and base shear in the TMD-controlled and uncontrolled adjacent buildings. Finally, the Park-Ang damage index is evaluated for the structures with and without the TMD system. It has been found that the maximum pounding force, the maximum acceleration of the stories, the base shear, the drift ratio, and the number of collisions significantly reduce (or omit) in the presence of TMDs when the objective function is considered to be the minimization of the maximum pounding force. It is shown that the maximum pounding force generally declines to zero for

关键词： Tuned mass damper Pounding force Concentrated plasticity method optimization algorithm Nonlinear time history analysis Damage index

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A single layer 1-bit ultra-wideband conformal metasurface for RCS-reduction with polarization conversion

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JOURNAL OF PHYSICS D-APPLIED PHYSICS 2025年第8期58卷 085002-085002页

作者： Liu, Youya Jiang, Dapeng Fu, Yang Zhou, Xiaofeng Ding, Fan Yang, Helin Cent China Normal Univ Coll Phys Sci & Technol Wuhan 430000 Peoples R China China Ship Dev & Design Ctr Wuhan 430064 Peoples R China Hanjiang Natl Lab Wuhan 430070 Peoples R China

This paper proposes a single layer 1-bit linear polarization converter for reducing the ultra-wideband radar cross section (RCS) of both planar and conformal surfaces. The layout of the supercells was optimized using the simulated annealing algorithm, achieving an average RCS reduction of over 10 dB in the range of 6.39-18.1 GHz in the planar case. By conforming the metasurface with cylinders of different radius of curvature, the result still meets the requirement of a 10 dB reduction within the bandwidth, which solves the expansion of the RCS reduction from planar to curved surfaces. Sample was prepared and experiments were conducted. The test results were consistent with the simulation results, indicating that the designed metasurface has potential applications in electromagnetic stealth for large targets.

关键词： polarization conversion RCS reduction optimization algorithm conformal ultra-wideband

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Optimized control of parallel heat pump units based on the satin bower bird algorithm in a distributed architecture

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CASE STUDIES IN THERMAL ENGINEERING 2025年 69卷

作者： Zhang, Sijia Xi, Jiangtao Zhao, Anjun Liu, Jun Zhang, Yuping Shaanxi Normal Univ Sch Comp Sci Xian 710119 Peoples R China China Northwest Architecture Design & Res Inst Co 98 Wenjing Rd Xian 710018 Peoples R China Xian Univ Architecture & Technol Sch Bldg Serv Sci & Engn Xian 710055 Peoples R China Shaanxi Coal Geol Grp Co Ltd Minist Nat Resources Key Lab Coal Resources Explorat & Comprehens Utili Xian 710021 Peoples R China

Compared to traditional shallow borehole heat exchanger (BHE), medium-deep borehole heat exchanger (MDBHE) exhibits significant advantages in heat extraction capability. Utilizing MDBHE as the heat source for heating systems can effectively achieve primary energy savings. However, in building heating applications, MDBHE is generally combined with heat pump units, which typically operate in parallel. The optimal load distribution of parallel heat pump units is a critical issue in MDBHE heating systems. Reasonable control to meet load demands is of great importance for the energy-efficient operation of the system. To address these issues, this study first establishes and compares optimization models of centralized and distributed systems targeting parallel heat pump units. The Distributed Satin Bowerbird optimization (DSBO) algorithm is proposed to address the load optimization distribution problem in parallel heat pump units within MDBHE. Experimental results confirm the robustness of the D-SBO algorithm, achieving up to a 23.1% improvement in COP. In case 1, the standard deviations of D-SBO range from 0.05 to 0.65 under a load demand of 40% to 90%. In case 2, the standard deviations range from 0.09 to 2.67 with a load demand of 70% to 90%. While D-SBO yields results comparable to DCSA, it demonstrates superior stability. Additionally, D-SBO provides significant energy savings, ranging from 3.90 kW to 140.38 kW in case 1 compared to GA, and from 1.00 kW to 165.00 kW in case 2 compared to GA and PSO.

关键词： Medium-deep borehole heat exchanger optimization algorithm Distributed energy system Optimal configuration Energy saving

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Lung cancer detection and classification using optimized CNN features and Squeeze-Inception-ResNeXt model

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COMPUTATIONAL BIOLOGY AND CHEMISTRY 2025年 117卷 108437页

作者： Lakshmi, G. Geethu Nagaraj, P. Kalasalingam Acad Res & Educ Dept Comp Sci & Engn Srivilliputhur Tamil Nadu India

Lung cancer, with its high mortality rate, is one of the deadliest diseases globally. The alarming increase in lung cancer deaths and its widespread prevalence have led to the development of various cancer control research and early detection methods aimed at reducing mortality rates. Effective diagnostic techniques are crucial for lowering lung cancer incidence, as early detection significantly impacts treatment success. Human error can often impede accurate identification of lung nodules, in which Computer-Aided Diagnostic (CAD) systems are utilized. These systems help radiologists by automating diagnostic processes and improving accuracy of detecting and classifying malignancies. This paper aims to develop a deep learning approach for classifying lung diseases using chest Computed Tomography (CT) scan images. The approach starts with image pre-processing, including color space conversion, data augmentation, resizing, and normalization. Feature extraction is carried out using a Convolutional Neural Network (CNN) optimized with Slime Mould algorithm (SMA). For classification, a novel approach combining Squeeze-Inception V3 with ResNeXt, referred to as Squeeze-Inception-ResNeXt, is proposed. The Squeeze-Inception-ResNeXt model benefits from reduced computational cost while maintaining high performance in classifying lung diseases. This model categorizes lung diseases into Adenocarcinoma, Large Cell Carcinoma, and Squamous Cell Carcinoma. Additionally, SMA is utilized in training the Squeeze-InceptionResNeXt model. Experimental results show that Squeeze-Inception-ResNeXt surpasses traditional models, with an accuracy of 97.7 %, sensitivity of 98.1 %, and specificity of 97.4 %.

关键词： CT-scan Deep learning Lung cancer Feature extraction optimization algorithm

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Optimal Multiobjective Operation of Multicarrier Energy Hub Taking Energy Buffering Into Account

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INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS 2025年第1期2025卷

作者： Mohammadi-Zaferani, Mohammad-Mehdi Ebrahimi, Reza Ghanbari, Mahmood Islamic Azad Univ Dept Elect Engn Gorgan Branch Gorgan Iran

This paper introduces a pioneering model for short-term planning of an energy hub (EH) that goes beyond traditional approaches by considering a comprehensive multicarrier energy system. The proposed model focuses on minimizing energy buffering costs while ensuring system operation and optimizing economic performance. The novelty of this study lies in its integrated approach, which simultaneously addresses operational efficiency, energy storage requirements, and overall system performance. The EH in this study is modeled as a prosumer within a day-ahead energy market, where both inflows and outflows of energy are optimized. The system's capability to interact with upstream energy networks, including gas, heat, and electricity, is a critical aspect of the model. This interaction is managed through various technologies that enhance the hub's ability to meet local demands efficiently. By employing an advanced improved particle swarm optimization (IPSO) algorithm, this model solves the complex multiobjective optimization problem inherent in EH management. The proposed model's effectiveness is validated through extensive simulation on a test system, where its performance is compared with conventional heuristic optimization algorithms. The results demonstrate the superior efficiency and applicability of the IPSO algorithm, confirming that the proposed model offers a significant advancement in the field of sustainable energy management.

关键词： energy buffering energy hub energy management energy storage system optimization algorithm

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Compressive strength prediction of sleeve grouting materials in prefabricated structures using hybrid optimized XGBoost models

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CONSTRUCTION AND BUILDING MATERIALS 2025年 476卷

作者： Wu, Yanqi Cai, Daqing Gu, Sheng Jiang, Nan Li, Shengli Zhengzhou Univ Sch Civil Engn Zhengzhou 450001 Peoples R China Kunshan Construct Engn Qual Testing Ctr Kunshan 215337 Peoples R China Zhengzhou Univ Sch Ecol & Environm Zhengzhou 450001 Peoples R China

The compressive strength of the grouting material inside the grouting sleeve is a key parameter in ensuring the safety and durability of the grouting sleeve connections in prefabricated assembled structures. To accurately estimate the compressive strength of the grouting material inside the grouting sleeve, this paper proposes a hybrid eXtreme Gradient Boosting (XGBoost) model based on optimization algorithms for hyper-parameter selection, using the surface hardness and age of the specimens as inputs. The XGBoost model optimized with three optimization algorithms accurately outputs the compressive strength of the grouting material on 213 sets of actual measured data samples. The results show that compared with the whale optimization algorithm (WOA) and sparrow search algorithm (SSA), the XGBoost model optimized by Bayesian optimization (BO) has higher prediction accuracy and smaller prediction errors, and is recommended as the preferred model for predicting the compressive strength of grouting material inside the sleeve. Additionally, the importance and influence of the two input variables on the output target were analyzed.

关键词： Prefabricated structures Sleeve grouting material Compressive strength Machine learning optimization algorithm

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Swarm Intelligent Search and Rescue method for economic emission load dispatch of renewable integrated power system considering uncertainty

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SWARM AND EVOLUTIONARY COMPUTATION 2025年 95卷

作者： Mishra, Shilpa Shaik, Abdul Gafoor Mahela, Om Prakash IIT Jodhpur Dept Elect Engn Jheepasani India IIT Jodhpur Ctr Emerging Technol Sustainable Dev Ctr IoT & Applicat Dept Elect Engn Jheepasani India RVPN Power Syst Planning Div Jaipur India

In the realm of power systems, Economic Emission Load Dispatch (EELD) problem is one of the most important bi-objective optimisation problem, associated with high complexity and non-linearities. This research proposes a novel metaheuristic optimization approach hybridizing the PSO and SAR and abbreviated as Swarm Intelligent Search and Rescue method (SISAR). It utilizes the features of Particle Swarm optimization algorithm to strengthen the global searching capability of original SAR algorithm. SISAR overcomes the drawback of SAR of getting trapped into local minima by utilizing velocity-based position update concept of PSO to improve the overall convergence. SISAR approach is initially evaluated on 10-unit, 2000 MW and 6-unit, IEEE30 bus standard test systems. Results are compared with advanced algorithms such as SAR, PSO, GWO, WOA, GA, DE and MFO in order to prove its superiority. Subsequent to the establishment of the proposed algorithm on system without renewable sources, it is further applied to a RE integrated power system comprising of six thermal units, 1 wind and 1 solar unit. Here, uncertainty due to RESs is dealt using a 2-stage uncertainty handling approach to obtain more accurate and feasible EELD solution. Robustness of the uncertainty handling approach is established by investigating the impact of different penetration levels of RE sources on cost and emission while solving EELD. A reduction of 1.67 % in cost and 2.56 % in emission have been achieved by proposed SISAR algorithm as compared to SAR (next competitive method) while performing EELD on RE integrated system with all sources.

关键词： Swarm intelligent search and rescue Economic emission load dispatch optimization algorithm Renewable energy sources Uncertainty Wind Solar

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Efficient Multi-User Resource Allocation for Urban Vehicular Edge Computing: A Hybrid Architecture Matching Approach

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2025年第1期74卷 1811-1816页

作者： Xie, Hongyang Liu, Haoqiang Chen, Huiming Feng, Shaohan Wei, Zhaobin Zeng, Yonghong Southeast Univ Sch Artificial Intelligence Nanjing 211102 Peoples R China Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China Univ Sci & Technol Beijing Sch Intelligence Sci & Technol Beijing 100083 Peoples R China Zhejiang Gongshang Univ Sch Informat & Elect Engn Hangzhou 314423 Peoples R China Sichuan Univ Coll Elect Engn Chengdu 610017 Peoples R China Agcy Sci Technol & Res Inst Infocomm Res Singapore 138632 Singapore

Advanced in the proliferation of the Internet of Things (IoT), a plethora of functions have been integrated in vehicular networks and thereby transfered it into a smart network. However, the contradiction between the limited on-vehicle computing resource and the massive data collected by these IoT devices hinders the broader adoption of vehicular network as a vast variety of on-vehicle applications are latency-sensitive. To address this issue, vehicular edge computing has become a promising technology as it can offload a large number of tasks from its proximal vehicles. However, the offloading methods recently utilized are inefficient while dealing with multi-user vehicular networks under dynamic scenarios. To design a superior offloading method that can effectively and efficiently offload tasks from vehicles to servers, multiple objectives and constraints with various topologies should be considered. In this paper, instead of constructing a typical multi-user and multi-server vehicular edge computing scenario, a complex scenario with more uncertainties, i.e. urban scenario, is modeled. We propose a Hybrid Architecture Matching algorithm (HAMA) to minimize the average time latency subject to the constraint on energy consumption and evaluate the proposed algorithm in the above two scenarios. Moreover, HAMA is constructed based on hybrid centralized-distributed architecture, which can process the centralized collected information on a distributed manner. Experimental results demonstrate that the matching algorithm can significantly reduce average time latency, achieving up to a 68% improvement compared to local execution.

关键词： Servers Delays Computational modeling Vehicle dynamics Urban areas Roads Handover Vehicular edge computing matching algorithm resource allocation optimization algorithm

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Motion Generation Around Obstacles: A Multidimensional Sampling-Based Planner

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2025年

作者： Xie, Zongwu Wang, Zhengpu Cao, Baoshi Liu, Yang Sun, Wandong Xie, Guanghu Ji, Yiming Ma, Boyu Harbin Inst Technol State Key Lab Robot & Syst Harbin 150080 Peoples R China

The quintic polynomial trajectory of the space manipulators holds significant value for the transmitting of ground-based data. In practice, it is frequently employed for large-scale transfer motions. However, this approach leads to an uncontrollable end-effector trajectory, which in turn restricts the applicability of various trajectory planning techniques reliant on end-effector trajectories. Sampling-based motion planner can be used to plan polynomial joint trajectories and optimize specific objectives, such as obstacle avoidance. Nevertheless, existing approaches either fix the sample dimensionality or neglect dimensional optimization. This article presents an enhancement based on genetic algorithms, introducing coding and crossover mechanisms for samples of varying dimensions. The trajectory planner performs search and optimization on a multidimensional sample set, thereby expanding the search domain and overcoming dimensional constraints. The mutual inspiration among samples of different dimensions enhances the search capabilities of the algorithm, yielding superior results. This work focuses on an actual engineering problem, and through simulation and validation experiments, it demonstrates that the proposed trajectory planner can generate a safe and coordinated quintic polynomial joint trajectory for the manipulator.

关键词： Trajectory Encoding optimization optimization models Polynomials Genetic algorithms Space stations Search problems End effectors Collision avoidance Modified genetic algorithm (GA) optimization algorithm sampling-based motion planner (SBMP) space manipulators

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