Mixed fleet-based two-echelon vehicle routing optimization for cold chain logistics with diverse recharging strategies. by Yaling Li, Wenzhu Liao, Yuqi Huang

“…The presented model optimizes economic and environmental considerations to mitigate air pollution and reduce dependence on non-renewable energy sources while providing feasible routes. …”
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Robust techno-economic optimization of energy hubs under uncertainty using active learning with artificial neural networks by Aya M. A. Heikal, Shady H. E. Abdel Aleem, Ragab A. El-Sehiemy, Almoataz Y. Abdelaziz

“…The optimization approach under uncertainty provides robust predictive abilities across various scenarios, allowing the system to optimize energy management effectively, enhancing operational efficiency while minimizing overall energy losses, costs, and emissions. …”
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Optimization of Decision Support Technology for Offshore Oil Condition Monitoring with Carbon Neutrality as the Goal in the Enterprise Development Process. by Shiya Gao, Xin Guan, Xiaojing Cao, Zhili Bai, Caimeng Wang, Yun Zhan, Haiyang Yu

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Integrated Carbon Flow Tracing and Topology Reconfiguration for Low-Carbon Optimal Dispatch in DG-Embedded Distribution Networks by Rao Fu, Guofeng Xia, Sining Hu, Yuhao Zhang, Handaoyuan Li, Jiachuan Shi

“…To efficiently solve this complex constrained model, a novel Q-learning enhanced Moth Flame Optimization (QMFO) algorithm was proposed. QMFO synergized the global search capability of the Moth Flame Optimization (MFO) algorithm with the adaptive decision-making of Q-learning, embedding an adaptive exploration strategy to significantly enhance solution efficiency and accuracy for multi-objective problems. …”
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A Deep Reinforcement Learning-Based Speed Optimization System to Reduce Fuel Consumption and Emissions for Smart Cities by Yıldıray Yiğit, Murat Karabatak

“…This method provides safer, more comfortable travel while lowering emissions and fuel consumption. Simulations with various scenarios prove the Deep Q-Network (DQN) algorithm’s success in adjusting speed according to traffic lights. …”
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Determination of Sequential Well Placements Using a Multi-Modal Convolutional Neural Network Integrated with Evolutionary Optimization by Seoyoon Kwon, Minsoo Ji, Min Kim, Juliana Y. Leung, Baehyun Min

“…This complex multi-million-dollar problem involves optimizing multiple parameters using computationally intensive reservoir simulations, often employing advanced algorithms such as optimization algorithms and machine/deep learning techniques to find near-optimal solutions efficiently while accounting for uncertainties and risks. …”
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Deep Reinforcement Learning-Enabled Trajectory and Bandwidth Allocation Optimization for UAV-Assisted Integrated Sensing and Covert Communication by Donghao Li, Binfang Du, Zhiquan Bai

“…The SAC-CC algorithm maximizes the CCTR by dynamically allocating bandwidth for sensing and communication tasks while adjusting the UAV’s trajectory to manage energy consumption. …”
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Analysis and mitigation of greenhouse gases by replacing traditional energy with a hybrid energy system using battery optimization by Madhusudhanan Sampath, Amalorpava Mary Rajee Samuel, Yamuna Devi M.M, Senthil Murugan

“…The paper presents an improved Lyapunov optimisation (LO) algorithm to optimally estimate the number of photovoltaic panels and battery banks for enhanced energy management. …”
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Balancing Human Mobility and Health Care Coverage in Sentinel Surveillance of Brazilian Indigenous Areas: Mathematical Optimization Approach by Juliane Fonseca Oliveira, Adriano O Vasconcelos, Andrêza L Alencar, Maria Célia S L Cunha, Izabel Marcilio, Manoel Barral-Netto, Pablo Ivan P Ramos

“…To optimize sentinel site selection, we implemented a linear optimization algorithm that maximizes (1) Indigenous region representation and (2) human mobility coverage. …”
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Adaptive Trajectory Optimization for UAV-IRS Systems in 6G Thz Networks Using Multi Agent-DRL by Elmadina Nahla Nur, Saeed Rashid A., Saeid Elsadig, Awouda Ala Eldin, Mujlid Hana M., Elshafie Hashim

“…Given the non-convex and NP-hard nature of the problem, traditional optimization methods are insufficient. To tackle this, we propose a Multi-Agent Deep Reinforcement Learning (MADRL) algorithm, which provides an efficient, low-complexity solution for trajectory optimization. …”
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Machinability Assessment and Multi-Objective Optimization of Graphene Nanoplatelets-Reinforced Aluminum Matrix Composite in Dry CNC Turning by Nikolaos A. Fountas, Dimitrios E. Manolakos, Nikolaos M. Vaxevanidis

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Leveraging ensemble learning-based stock preselection with multiobjective investment optimization for stepwise decision-supported portfolio management by Jui-Sheng Chou, Tran-Bao-Quyen Pham

“…This stage leverages a newly developed algorithm tailored to address the expanded mean–variance problem, effectively decomposing and optimizing it while balancing multiple objectives. …”
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System identification and robust PID controller tuning of quarter car suspension system using hybrid optimization techniques by S. Sakthiya Ram, C. Kumar, R. Saravanakumar, David Banjerdpongchai

“…After validating the resulting dynamic model, various PID controllers are designed using standard metaheuristic algorithms—Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Simulated Annealing (SA). …”
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An Improved Particle Swarm Optimization and Adaptive Neuro-Fuzzy Inference System for Predicting the Energy Consumption of University Residence by Stephen Oladipo, Yanxia Sun, Oluwatobi Adeleke

“…The model input variables are wind speed, temperature, and humidity, with the output being the equivalent energy consumption for the student housing. While the particle swarm optimization (PSO) technique is versatile and widely used, it falls short by exhibiting premature convergence. …”
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Optimizing Energy and Cost Performance in Residential Buildings: A Multi-Objective Approach Applied to the City of Patras, Greece by Dionyssis Makris, Anastasia Antzoulatou, Alexandros Romaios, Sonia Malefaki, John A. Paravantis, Athanassios Giannadakis, Giouli Mihalakakou

“…The results confirmed the algorithm’s robustness in producing technically feasible and non-dominated solutions, while also highlighting the sensitivity of optimization outcomes to hyperparameter tuning. …”
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Optimizing Energy Efficiency in Cloud Data Centers: A Reinforcement Learning-Based Virtual Machine Placement Strategy by Abdelhadi Amahrouch, Youssef Saadi, Said El Kafhali

“…To address this issue, we propose a novel energy-efficient virtual machine (VM) placement strategy that integrates reinforcement learning (Q-learning), a Firefly optimization algorithm, and a VM sensitivity classification model based on random forest and self-organizing map. …”
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Dynamic Sensor-Based Data Management Optimization Strategy of Edge Artificial Intelligence Model for Intelligent Transportation System by Nu Wen, Ying Zhou, Yang Wang, Ye Zheng, Yong Fan, Yang Liu, Yankun Wang, Minmin Li

“…To address these issues, we propose an automatic sensor-based data loading and unloading optimization strategy for algorithm models. This strategy is designed for artificial intelligence (AI) application systems that leverage edge computing. …”
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Fuzzy logic-based particle swarm optimization for integrated energy management system considering battery storage degradation by Oladimeji Ibrahim, Mohd Junaidi Abdul Aziz, Razman Ayop, Ahmed Tijjani Dahiru, Wen Yao Low, Mohd Herwan Sulaiman, Temitope Ibrahim Amosa

“…The FLB-PSO algorithm proficiently manages energy sources while addressing complexities associated with battery storage degradation. …”
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Developing a novel approach for passive damped LCL filter and controller parameter design using PSO algorithm in VSC-based islanded microgrids by Yared Bekele Beyene, Getachew Biru Worku, Lina Bertling Tjernberg

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Real-Time Energy Management in Microgrids: Integrating T-Cell Optimization, Droop Control, and HIL Validation with OPAL-RT by Achraf Boukaibat, Nissrine Krami, Youssef Rochdi, Yassir El Bakkali, Mohamed Laamim, Abdelilah Rochd

“…The T-Cell algorithm, inspired by adaptive immune system dynamics, optimizes global power distribution through the MAS platform, while droop control ensures local voltage stability via autonomous adjustments by distributed energy resources (DERs). …”
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