A Flexible Interconnected Distribution Network Power Supply Restoration Method Based on E-SOP by Lin Jiang, Canbin Wang, Wei Qiu, Hui Xiao, Wenshan Hu

“…Subsequently, a dual-objective optimization function is formulated to maximize the sum of nodal active load restoration while minimizing network losses. …”
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Transactive energy management for efficient scheduling and storage utilization in a grid-connected renewable energy-based microgrid by Peter Anuoluwapo Gbadega, Olufunke Abolaji Balogun

“…This study presents an advanced Transactive Energy Management (TEM) approach employing the Slime Mould Algorithm (SMA) to optimize scheduling and storage utilization in grid-connected renewable energy microgrids. …”
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Advancing multi-categorization and segmentation in brain tumors using novel efficient deep learning approaches by Nadenlla RajamohanReddy, G. Muneeswari

“…Using the Binary Chaotic Transient Search Optimization (BCTSO) Algorithm, the most pertinent features in terms of shape, texture, and colour are chosen to minimize complexity. …”
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A Multi-Objective Sensor Placement Method Considering Modal Identification Uncertainty and Damage Detection Sensitivity by Xue-Yang Pei, Yuan Hou, Hai-Bin Huang, Jun-Xing Zheng

“…To address this challenge, this study proposes a multi-objective sensor placement optimization method based on the Non-Dominated Sorting Genetic Algorithm. …”
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Real-Time Estimation of the State of Charge of Lithium Batteries Under a Wide Temperature Range by Da Li, Lu Liu, Chuanxu Yue, Xiaojin Gao, Yunhai Zhu

“…Additionally, the cat swarm optimization (CSO) algorithm optimizes the noise covariance matrices of both filters, thereby enhancing the precision of parameter identification and <i>SOC</i> estimation. …”
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A Judging Scheme for Large-Scale Innovative Class Competitions Based on Z-Score Pro Computational Model and BP Neural Network Model by Zhuoting Yu, Hongzhong Deng, Shuaiwen Tang

“…The model employs a genetic algorithm to maximize the overlap between experts while ensuring a balanced distribution of evaluation tasks, thus reducing the entropy generated by imbalances in the process. …”
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A Bi-Layer Collaborative Planning Framework for Multi-UAV Delivery Tasks in Multi-Depot Urban Logistics by Junfu Wen, Fei Wang, Yebo Su

“…The novelty of this work lies in the seamless integration of an enhanced genetic algorithm and tailored swarm optimization within a unified two-tier architecture. …”
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Toward model-based individualized fitting of hip-flexion exosuits for persons with unilateral transfemoral amputation by Finn G. Eagen, Nicholas P. Fey

“…A user-specific device profile is generated via a three-axis moment-matching optimization using an interior-point algorithm. We employ two related cost functions that reflect an active and passive form of the device. …”
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Improved grey wolf optimization for parameter extraction of polycrystalline and mono crystalline solar PV panels using double diode model by Madhusudana Rao Ranga, Venkateswara Rao Bathina, Srikumar Kotni

“…In this paper an enhanced version of the Grey Wolf Optimizer (GWO) algorithm called Improved Grey Wolf Optimizer (IGWO) has been proposed for extraction of parameters. …”
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Stiffness Optimization for Hybrid Electric Vehicle Powertrain Mounting System in the Context of NSGA II for Vibration Decoupling and Dynamic Reaction Minimization by Zhanpeng Fang, Qihang Li, Lei Yao, Xiaojuan Hu

“…The 6-DOF decoupling of the whole suspension system is optimized and the dynamic reaction transmitted to the body is minimized. …”
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An Optimization Framework for Waste Treatment Center Site Selection Considering Nighttime Light Remote Sensing Data and Waste Production Fluctuations by Junbao Xia, Yanping Liu, Haozhong Yang, Guodong Zhu

“…Using Beijing as a case study, the gradient boosting regression algorithm yielded a prediction accuracy of 92%. Furthermore, in light of the substantial costs associated with waste recovery route planning and site selection for treatment facilities, this research further devised a location and distribution framework for waste treatment centers based on high-precision predictions of waste production while employing multi-objective evolutionary algorithms (MOEAs) alongside the non-dominated sorting genetic algorithm II (NSGA-II) for optimization. …”
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Research on unmanned mining truck path planning based on grey wolf optimization adaptive hybrid A* and artificial potential field by HAN Yanling, LYU Jinke, ZHOU Guofeng, ZHANG Like, ZHANG Yun

“…The proposed method emploied the grey wolf optimization algorithm to adaptively adjust the key parameters of the hybrid A*(HA*) algorithm, achieving a multi-objective balance among path length, smoothness, and planning time. …”
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Performance Comparison of Multi-Objective Optimizers for Dynamic Balancing of Six-Bar Watt Linkages Using a Fully Cartesian Model by María T. Orvañanos-Guerrero, Claudia N. Sánchez, Luis Eduardo Robles-Jiménez, Sara Carolina Gómez-Delgado

“…A wide set of optimization methods is analyzed and compared, and among them, the S-Metric Selection Evolutionary Multi-objective Optimization Algorithm (SMS-EMOA) demonstrates superior performance. …”
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Generic Method for Social–Environmental System Boundary Delineation—An Amalgamation of Spatial Data Integration, Optimization, and User Control for Resource Management by Mohammad Shahriyar Parvez, Xin Feng

“…The framework also leverages the SKATER algorithm for precise regionalization, ensuring spatial continuity and compactness while allowing user control over region selection and data dimensions. …”
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Satellite- and Cache-Assisted UAV: A Joint Cache Placement, Resource Allocation, and Trajectory Optimization for 6G Aerial Networks by Dinh-Hieu Tran, Symeon Chatzinotas, Bjorn Ottersten

“…In this context, we aim to maximize the minimum achievable throughput per ground user (GU) by jointly optimizing cache placement, the UAV&#x2019;s resource allocation, and trajectory while cache capacity and flight time are limited. …”
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Research on Q-Learning-Based Cooperative Optimization Methodology for Dynamic Task Scheduling and Energy Consumption in Underwater Pan-Tilt Systems by Shan Tao, Lei Yang, Xiaobo Zhang, Shengya Zhao, Kun Liu, Xinran Tian, Hengxin Xu

“…Simulation results demonstrate that compared to fixed-duration observation schemes, the proposed optimization strategy achieves a 11.11% improvement in monitoring effectiveness while achieving 16.21% energy savings.…”
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Joint Optimization of Resource Allocation and Task Offloading Strategies in Multi-Cell Dynamic MEC Systems Using Multi-Agent DRL by Yuntao Hu, Ming Chen, Haowen Sun, Yinlu Wang, Yihan Cang

“…The energy consumption minimization is achieved by jointly optimizing the task offloading proportions, transmit power allocations, and computational resource distributions while ensuring the overall deadline constraints and the minimum processing size requirements in each scheduling cycle. …”
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Efficient resource allocation for D2D-enabled social IoT networks: A tripartite and time-scale optimization approach by Saurabh Chandra, Rajeev Arya, Maheshwari Prasad Singh

“…The problem is solved in two stages: a tripartite graph-based resource allocation stage and a time-scale optimization stage. The proposed approach prioritizes maintaining Quality of Service (QoS) and resource allocation to minimize power consumption while maximizing sum throughput. …”
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Optimizing UAV Location for Deployment in Cell-Free Massive MIMO Networks Using a Soft Actor-Critic Reinforcement Learning by Okello Kenneth, Elijah Mwangi, Dominic Bernard Onyango Konditi

“…The uncrewed aerial vehicles (UAVs) communication network has recently garnered significant attention and motivation in academic and industrial research due to its flexible mobility concept in providing dynamic coverage in hotspot or congested areas with frequent user demand. While UAVs offer reliable connectivity in such challenging scenarios, integrating them with Cell-Free Massive MIMO (CF-mMIMO) networks presents severe issues in optimal deployment and coverage efficiency. …”
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Optimizing the train timetable in a high-speed rail corridor: The implications on departure time, fare cost and seat preference of passengers. by Zhipeng Huang, Limin Yang, Jinlian Li, Tao Zhang, Zixian Qu, Yusen Miao

“…A bi-level programming model is formulated: the upper level optimizes train operations and fare structures, while the lower level employs user equilibrium (UE) theory to distribute OD passenger demands across trains. …”
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