Two-layer Optimization Scheduling for Off-grid Microgrids Based on Multi-agent Deep Policy Gradient by Huicong FAN, Zhiguo DUAN, Zhiyong CHEN, Shijia ZHU, Hang LIU, Wenxiao LI, Yang YANG

“…The lower-level model optimizes slow-regulating discrete devices based on mixed-integer second-order cone programming, while the upper-level model optimizes fast-regulating continuous devices using a multi-agent deep policy gradient algorithm. …”
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Development and Optimization of a Novel Semi-Submersible Floater for Floating Wind Turbines in the South China Sea by Yiming Zhong, Wenze Liu, Wei Shi, Xin Li, Shuaishuai Wang, Constantine Michailides

“…Using a differential evolution algorithm, the optimization process adjusts column diameter and spacing while considering motion response and steel usage constraints. …”
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Sentence Embedding Generation Framework Based on Kullback–Leibler Divergence Optimization and RoBERTa Knowledge Distillation by Jin Han, Liang Yang

“…First, a sentence embedding generation method based on Kullback–Leibler Divergence (KLD) optimization is proposed, which enhances semantic differentiation between sentence vectors, thereby improving the accuracy of textual similarity computation. …”
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Optimizing HVAC&R System Efficiency and Comfort Levels Using Machine Learning-Based Control Methods by Suroor M. Dawood, Raad Z. Homod, Alireza Hatami

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Battery Energy Storage System Optimal Sizing in a Battery Electric Vehicle Fast Charging Infrastructure by Pedro Félix, Luís A. C. Roque, Ismael Miranda, António Gomes

“…This sizing is a result of an optimization based on the interior point algorithm, where the objective is to minimize the costs of maintenance, operation, and installation of a BESS, while properly modelling the different resources such as the BESS, the charging station and EV charging and PV generation. …”
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Optimization of Bandwidth Allocation and UAV Placement in Active RIS-Assisted UAV Communication Networks with Wireless Backhaul by Thi-Thuy-Minh Tran, Binh-Minh Vu, Oh-Soon Shin

“…The resulting optimization problem is highly non-convex, posing significant challenges. …”
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An enhanced moth flame optimization extreme learning machines hybrid model for predicting CO2 emissions by Ahmed Ramdan Almaqtouf Algwil, Wagdi M. S. Khalifa

“…The model integrates the Gaussian mutation and shrink mechanism-based moth flame optimization (GMSMFO) algorithm with an extreme learning machine (ELM). …”
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Dynamic Modeling of a Kaplan Hydroturbine Using Optimal Parametric Tuning and Real Plant Operational Data by Hong Wang, Sunil Subedi, Wenbo Jia

“…To solve this issue, this paper first formulates a six-coefficient Kaplan hydroturbine model and then proposes a parametric optimization tuning framework based on the Nelder–Mead algorithm for adaptive dynamic learning of the six-coefficients so as to build models that describe the turbine. …”
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Exploration design for Q-learning-based adaptive linear quadratic optimal regulators under stochastic disturbances by Vina Putri Virgiani, Shiro Masuda

“…The decaying method gradually reduces the exploration signal over time, enabling effective initial exploration while stabilizing as the system approaches optimal performance. …”
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Balancing conflicting objectives in pre-salt reservoir development: A robust multi-objective optimization framework by Auref Rostamian, Amir Davari Malekabadi, Marx Vladimir De Souda Miranda, Vinicius Edurado Botechia, Denis José Schiozer

“…The study focuses on maximizing expected monetary value (EMV) and the net present value of RM4 considering economic uncertainty (NPVeco of RM4), of the most pessimistic scenario among the RMs. The optimization variables are location, type (injection or production), and number of wells, while the non-dominated sorting genetic algorithm II (NSGA-II) is employed for multi-objective optimization. …”
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Prediction and optimization of hardness in AlSi10Mg alloy produced by laser powder bed fusion using statistical and machine learning approaches by İnayet Burcu Toprak

“…In addition, Machine Learning algorithms were applied to predict hardness values. …”
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Optimizing a Dynamic Vehicle Routing Problem with Deep Reinforcement Learning: Analyzing State-Space Components by Anna Konovalenko, Lars Magnus Hvattum

“…<i>Background:</i> The dynamic vehicle routing problem (DVRP) is a complex optimization problem that is crucial for applications such as last-mile delivery. …”
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Enhanced Particle Swarm Optimization-Based Feeder Reconfiguration Considering Uncertain Large Photovoltaic Powers and Demands by Ying-Yi Hong, Faa-Jeng Lin, Fu-Yuan Hsu

“…Moreover, a two-stage algorithm is developed, in which the uncertain bus loads and PV powers are clustered by fuzzy-c-means to gain representative scenarios; optimal reconfiguration is then achieved by a novel mean-variance-based particle swarm optimization. …”
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Optimized Controller Design Using Hybrid Real-Time Model Identification with LSTM-Based Adaptive Control by Yeon-Jeong Park, Joon-Ho Cho

“…Our approach integrates an optimally adaptive Proportional–Integral–Derivative (PID) controller design algorithm that estimates the coefficients of the SOPTD model in the Smith Predictor control structure and adjusts the PID controller parameters dynamically. …”
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Second-order sliding mode optimization control of an inverted pendulum system based on fuzzy adaptive technology by Xuefeng Leng

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Machining effects and multi-objective optimization in Inconel 718 turning with unitary and hybrid nanofluids under MQL by Paresh Kulkarni, Satish Chinchanikar

“…The cutting parameters were optimized by combining the Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS) and genetic algorithm. …”
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Local Structure Optimization Design of Floating Offshore Wind Turbine Platform Based on Response Surface Analysis by Yajun Ren, Mingxuan Huang, Jungang Hao, Jiazhi Wang, Shuai Li, Ling Zhu, Haisheng Zhao, Wei Shi

“…The optimization results indicate that the maximum stress of the optimized model is reduced by 22.12% compared to the original model, while maintaining the same mass, centroid, and other mass-related parameters. …”
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RM-MOCO: A Fast-Solving Model for Neural Multi-Objective Combinatorial Optimization Based on Retention by Huiqing Wei, Fei Han, Qing Liu, Henry Han

“…An industry-standard deep reinforcement learning algorithm is used to train RM-MOCO. Experimental results show that, while ensuring the quality of problem solving,the proposed method significantly outperforms some other methods in terms of the speed of solving MOCO problems.…”
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Multiobjective optimization of external shading for west facing university dormitories in Kunming considering solar radiation and daylighting by Tong Chen, Liangbin Tan

“…A parametric modeling and simulation approach was adopted, and Pareto-optimal solutions were generated using the Non-dominated Sorting Genetic Algorithm II (NSGA-II). …”
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Multi-objective optimization to manage reservoir water quality and quantity via selective withdrawal and watershed control by Habib Abbasi, Mohammad taghi Aalami, Mohammad Hossein Niksokhan, Leila Malekan

“…Soil and Water Assessment Tool (SWAT) model was linked to a reservoir water quality simulation model (CE-QUAL-W2), the linked watershed-reservoir model was coupled with Multi-Objective Particle Swarm Optimization algorithm (MOPSO) to find the best set of decisions to optimize the reservoir outflow quality and quantity objectives. …”
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