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Dream optimization algorithm (DOA): A novel metaheuristic optimization algorithm inspired by human dreams and its applications to real-world engineering problems

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COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 2025年 436卷

作者： Lang, Yifan Gao, Yuelin North Minzu Univ Sch Math & Informat Sci Yin Chuan 750021 Peoples R China Ningxia Prov Key Lab Intelligent Informat & Data P Yin Chuan 750021 Peoples R China

As optimization problems grow increasingly complex, traditional deterministic algorithms often struggle to address these challenges. Metaheuristic algorithms, with their flexibility and low problem dependency, have emerged as a competitive alternative. This paper introduces the Dream optimization algorithm (DOA), inspired by human dreams, which exhibit partial memory retention, forgetting, and logical self-organization characteristics that bear strong similarities to the optimization process in metaheuristic algorithms. DOA incorporates a foundational memory strategy, a forgetting and supplementation strategy to balance exploration and exploitation, and a dream-sharing strategy to improve the ability to escape local optima. The optimization process is divided into exploration and exploitation phases, yielding satisfactory optimization results. This paper qualitatively analyzes DOA's search history, exploration-exploitation capabilities, and population diversity, showing its ability to adapt to problems of varying complexity. Quantitative analysis using three CEC benchmarks (CEC2017, CEC2019, CEC2022) compares DOA against 27 algorithms, including CEC2017 champion algorithms. Results indicate that DOA outperforms all competitors, showcasing superior convergence, advancement, stability, adaptability, robustness, significance, and reliability. Additionally, DOA achieved optimal results in eight engineering constrained optimization problems and in the practical application of photovoltaic cell model parameter optimization, demonstrating its effectiveness and practicality. The source code of DOA is publicly accessible at https://***/matlabcentral/fileexchange/178419-dreamoptimization-algorithm-doa

关键词： optimization algorithm Metaheuristic Dream Benchmarks Constrained optimization Photovoltaic cell

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A breakthrough land-use-based carbon accounting framework for multi-scale evaluation: Coupling optimization algorithm and LCA

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SUSTAINABLE CITIES AND SOCIETY 2025年 120卷

作者： Zhao, Jing Xu, Shengnan Ren, Yujie Tang, Xiaolan Nanjing Forestry Univ Coll Landscape Architecture Nanjing 210037 Peoples R China China ENFI Engn Corp Beijing 100038 Peoples R China Nanjing Forestry Univ NFU Acad Chinese Ecol Progress & Forestry Studies Nanjing 210037 Peoples R China

Comprehensive, accurate, and efficient carbon accounting is essential for formulating carbon reduction strategies and assessing their effectiveness in urban areas. However, inconsistencies in carbon accounting results in urban areas often arise due to the lack of standardized methods, making the development of a unified, convenient, and multi-scale applicable carbon accounting framework critical. Currently, urban carbon accounting based on land use encounters challenges related to functional complexity, data barriers, and issues of precision and efficiency. To address these challenges, this study considered the spatiotemporal characteristics of life cycle carbon emissions and applied the optimization algorithm from operations research to derive the optimal distribution ratios of the employment population and various industry land uses, thereby overcoming the issues of functional complexity and data barriers in urban industry land uses. The equivalency factor method was utilized to construct a transportation carbon emission table, converting ground traffic volume into equivalent passenger car units, significantly improving carbon accounting efficiency. Local carbon emission factors were employed to further enhance the accuracy of carbon accounting. Based on these approaches, a unified, standardized, and multi-scale applicable urban carbon accounting framework was developed. Using Zhoupu Town in Shanghai, China, as a case study, the carbon emissions were calculated during the construction phase and over a one-year operational period. The research findings revealed that: a) Various industry and residential land uses were the primary carbon emission sources during both the construction and operational phases, with industry land uses contributing a larger share;b) Rail transit and public green spaces demonstrated considerable carbon reduction potential during the operational phase;c) The total carbon emissions in Zhoupu Town during the construction phase were approximate

关键词： Carbon accounting Land use Life cycle assessment (LCA) optimization algorithm IPCC factor method Shanghai

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Efficient slope reliability analysis using a surrogate-assisted normal search particle swarm optimization algorithm

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JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING 2024年第1期11卷 173-194页

作者： Yuan, Yi-li Hu, Chang-ming Li, Liang Xu, Jian Hou, Xu-hui Xian Univ Sci & Technol Sch Architecture & Civil Engn Xian 710055 Shaanxi Peoples R China Xian Univ Architecture & Technol Coll Civil Engn Xian 710055 Shaanxi Peoples R China Qinghai Univ Sch Civil Engn Xining 810016 Qinghai Peoples R China Qinghai Prov Key Lab Energy Saving Bldg Mat & Engn Xining 810016 Qinghai Peoples R China

Sampling of training data is the most important step in active learning slope reliability analysis, which controls the analysis accuracy. In this study, a novel surrogate-assisted normal search particle swarm optimization (SANSPSO) was proposed to enhance the accuracy and robustness of existing methodologies. In SANSPSO, the sampling process was considered a minimum problem with an objective function defined as the absolute value of the performance function. Initiated with a normal search paradigm and supplemented by three algorithm strategies, this approach seeks to preserve the continuity of the solution while refining the algorithm's efficacy and efficiency. To reduce computation cost, surrogate-assistance was used, in which a surrogate model substitutes the objective function in most iterations. This surrogate model evolves during the iteration process and ultimately replaces the actual performance function within Monte Carlo simulation. Finally, this study presents a comparative study with five state-of-the-art methods across four explicit problems and three engineering cases, where test data suggest that the SANSPSO methodology yields a 20% improvement in accuracy and a 30% rise in stability under different dimensional problems relative to the most efficacious of the alternate methods assessed because of the improved and more consistent prediction of limit state function. These findings substantiate the validity and robustness of the SANSPSO approach. Graphical Abstract

关键词： reliability analysis swarm intelligence limit state function sequential design of experiments probability of failure optimization algorithm

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A weighted switching sequence optimization algorithm for static output feedback control synthesis of nonlinear systems

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APPLIED MATHEMATICS AND COMPUTATION 2025年 489卷

作者： Gao, Jingjing Xie, Xiangpeng Liaocheng Univ Sch Math Sci Liaocheng 252059 Shandong Peoples R China Nanjing Univ Posts & Telecommun Sch Internet Things Nanjing 210023 Peoples R China

In this paper, the static output feedback (SOF) control synthesis of discrete-time Takagi-Sugeno (T-S) fuzzy systems is concerned upon homogeneous polynomial parameter dependent Lyapunov functions (HPPD-LFs). It is well known that SOF control always leads to inequality conditions with non-convexity, which makes the optimization problem intractable. To overcome this difficulty, a novel switching sequence convex optimization (SSCO) algorithm is proposed, which is upon the matrix decomposition concept and the inner approximation strategy to eliminate the non-convex terms formed by the controller and the slack variables. Unlike conventional methods, the controller acts as a direct optimization variable and does not require structural or multiplicative relationships between the slack variables, which opens up the possibility of obtaining improved results in terms of l(2) gain performance. In particular, more relaxed design conditions are obtained for SOF controller based on the weighted switching method by effectively utilizing the membership functions information. Finally, two simulation examples demonstrate the superiority of the developed SOF control scheme.

关键词： Fuzzy systems Homogeneous polynomial parameter dependent approach Weighted switching method optimization algorithm

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Front-End Parameter Identification Method Based on Adam-W optimization algorithm for Underwater Wireless Power Transfer System

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IEEE TRANSACTIONS ON POWER ELECTRONICS 2025年第4期40卷 6307-6318页

作者： Luo, Bo Wu, Huan Wang, Mengyao Wang, Fangrui Bai, Longlei Jiang, Chaoqiang You, Jiang Harbin Engn Univ Yantai Res Inst Yantai 264000 Peoples R China Harbin Engn Univ Coll Intelligent Syst Sci & Engn Harbin 150001 Peoples R China City Univ Hong Kong Dept Elect Engn Hong Kong Peoples R China

Monitoring coupler parameters in underwater wireless power transfer (UWPT) systems is crucial for improving the system transmission characteristics. Due to the eddy current effect, the equivalent circuits of the magnetic coupler are more complex and contain more parameters than those in air-based circuits. Traditional parameter identification methods, which rely on solving circuit matrix equations, often struggle with the complex UWPT systems or require significant computational resources to solve high-order multivariate equations. This article proposes a coupler parameter identification method based on the adaptive moment estimation with a weighted adjustment (Adam-W) optimization algorithm to address the multiparameter identification problems in the UWPT system. This method transforms the problem of solving high-order matrix equations into an optimization problem to address multiparameter identification problem caused by seawater eddy effect, which is challenging for traditional methods. In addition, it facilitates online monitoring of the characteristic parameters of the system's coupler by detecting the system's front-end input current and voltage without wireless communication and additional sensor modules, and exhibits better reliability in underwater environments. The experimental results show that the proposed Adam-W algorithm achieves a high parameter identification accuracy within 1 s and 94 iterations with an average error of 2.78%. Comparably, an average error of 56.74% is achieved by gradient descent and 41.49% by adaptive moment estimation, and 37.42% by particle swarm optimization in 150 iterations. The proposed Adam-W algorithm achieves higher accuracy in parameter identification of the UWPT system within a shorter time and demonstrates better applicability in seawater environments.

关键词： Coils Mathematical models Eddy currents Integrated circuit modeling Circuits optimization Parameter estimation Couplings Couplers Inductance Adaptive moment estimation (Adam) coupler parameter identification gradient descent (GD) inductive power transfer (IPT) optimization algorithm underwater wireless power transfer (UWPT)

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Enhanced COVID-19 optimization algorithm for Solving Multi-Objective Optimal Power Flow Problems with Uncertain Renewable Energy Sources: A Case Study of the Iraqi High-Voltage Grid

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ENERGIES 2025年第3期18卷 478-478页

作者： Albaaj, Basim Kaplan, Orhan Gazi Univ Dept Elect & Elect Engn TR-06570 Ankara Turkiye

The optimal power flow (OPF) problem is a critical component in the design and operation of power transmission systems. Various optimization algorithms have been developed to address this issue. This paper expands the use of the coronavirus disease optimization algorithm (COVIDOA) to solve a multi-objective OPF problem (MO-OPF), incorporating renewable energy sources as distributed generation (DG) across multiple scenarios. The main objective is to minimize fuel costs, emissions, voltage deviations, and power losses. Due to its non-convex nature and computational complexity, OPF poses significant challenges. While COVIDOA has been utilized to solve engineering problems, it faces difficulties with non-linear and non-convex issues. This paper introduces an enhanced version, the enhanced COVID-19 optimization algorithm (ENHCOVIDOA), designed to improve the performance of the original method. The effectiveness of the proposed algorithm is validated through testing on IEEE 30-bus, 57-bus, and 118-bus systems, as well as a real-world 28-bus system representing Iraq's standard Iraq super grid high voltage (SISGHV 28-bus). The two-point estimation method (TPEM) is also applied to manage uncertainties in renewable energy sources in some cases, leading to cost reductions and annual savings of ($70,909.344, $817,676.64, and $5,608,782.144) for the IEEE 30-bus, 57-bus, and reality 28-bus systems, respectively. Thirteen different cases were analyzed, and the results demonstrate that ENHCOVIDOA is notably more efficient and effective than other optimization algorithms in the literature.

关键词： optimization algorithm multi-objective OPF problem distributed generation TPEM standard Iraq super grid high voltage 28-bus

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Bistatic RCS Pattern Synthesis With a Receiver-Transmitter Metasurface and a Phase Distribution optimization algorithm

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MICROWAVE AND OPTICAL TECHNOLOGY LETTERS 2024年第12期66卷

作者： Sima, Boyu Xu, Yibang Li, Huangyan Wang, Xiang Zong, Zhiyuan Kang, Wei Wu, Wen Nanjing Univ Sci & Technol Sch Elect & Opt Engn Key Lab Near Range RF Sensing ICs & Microsyst NJUS Minist Educ Nanjing Peoples R China

This paper proposes a metasurface design method to achieve bistatic radar cross-section (RCS) pattern synthesis in space, based on a receiver-transmitter metasurface (RTMS) and a genetic algorithm for phase distribution optimization. Due to the slots on the ground between the adjacent metasurface elements, the unwanted interunit coupling effect is significantly reduced. Based on the theoretical analysis model of the phased array, the corresponding phase distribution of the required RCS pattern is optimized by a genetic algorithm. According to the required phase distribution, the corresponding RTMS units are designed, and the overall RTMS structure is finally constructed. The simulated results demonstrate that the electromagnetic (EM) waves scattered by the proposed RTMS are concentrated in the desired directions of 0 degrees and +/- 49 degrees which is consistent with the design intention. Both simulated and measured results verify that the proposed RTMS design methodology can realize the bistatic RCS control in space with a customized shape.

关键词： bistatic RCS synthesis metasurface optimization algorithm phase distribution receiver-transmitter

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Improved composite model using metaheuristic optimization algorithm for short-term power load forecasting

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ELECTRIC POWER SYSTEMS RESEARCH 2025年 241卷

作者： Hu, Xuhui Li, Huimin Si, Chen Shandong Univ Sch Elect Engn Jinan 250100 Peoples R China

Accurate short-term electric load forecasting contributes to operational efficiency, grid stability, and profitability in power systems and energy markets. With increasing complexity in grid operations due to renewable integration and demand fluctuations, enhancing forecasting precision, particularly at 15-minute intervals, has become essential. In this study, we propose a composite model, TCN-Self-Attention-BILSTM (TSAB), designed to improve load prediction accuracy by integrating multiple advanced neural network architectures. Specifically, the Temporal Convolutional Network (TCN) captures long-term dependencies, while a self-attention mechanism dynamically emphasizes key features, and the Bidirectional Long Short-Term Memory Network (BILSTM) establishes robust temporal relationships. To optimize the hyperparameters of TSAB efficiently, we introduce the Enhanced Triangular Topology Aggregation Optimizer (ETTAO), a novel approach for rapid and effective tuning for composite models such as TSAB. Additionally, to evaluate model predication accuracy and hyperparameter optimization, we present a new objective function that combines multiple evaluation metrics based on their physical significance, balancing model performance across key aspects of accuracy. Experimental validation on two benchmark datasets demonstrates that TSAB outperforms conventional models, including LSTM, TCN, and CNN-BILSTM-Attention, in both feature extraction and predictive accuracy. Together, TSAB, ETTAO, and the new evaluation function offer a comprehensive approach to improving prediction accuracy, hyperparameter optimization tuning, and model evaluation, contributing to more effective and reliable short-term load forecasting in the electric power sector, with implications for enhanced operational decision-making in the electric power sector.

关键词： Short-term load forecasting BILSTM Self-attention TCN optimization algorithm TSAB ETTAO

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A Novel Impedance Estimation Method for Determining Voltage Unbalance Contributions Based on an optimization algorithm

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IEEE ACCESS 2024年 12卷 145944-145954页

作者： Brito, Vinicius Henrique Farias de Oliveira, Jose Carlos Gregory, Raquel Cristina Filiagi Univ Fed Uberlandia Elect Engn Fac BR-38400902 Uberlandia Brazil Univ Fed Ceara Dept Elect Engn BR-60020181 Fortaleza Brazil

The problem of "unbalance responsibility-sharing" is a matter of great concern for utility and consumer entities due to the increase in voltage unbalances, especially in electrical distribution systems. This issue arises from the need to determine the individual contributions of the utility and the consumer to the total unbalance at the point of common coupling between the entities. In the literature, the Superposition Method stands out as an effective approach to solving this problem. However, this procedure requires utility and consumer negative-sequence impedances, which are difficult to obtain in real-life scenarios. There is a variety of impedance estimation methods to overcome this issue, with emphasis on non-invasive methods. According to the literature, some of these procedures have practical and accuracy limitations. Therefore, this paper proposes a novel non-invasive impedance estimation method, termed the Correlation Minimization Method (CMM), which is based, essentially, on an optimization algorithm. In addition, computational simulations and field tests were carried out to evaluate the performance of the proposed method in comparison with existing approaches. The results show that the proposed method performed satisfactorily, even in the most adverse situations.

关键词： Impedance Estimation Equivalent circuits Fluctuations Harmonic analysis Voltage measurement optimization Frequency modulation Voltage Vectors Power quality Impedance estimation method non-invasive method optimization algorithm power quality unbalance unbalance responsibility-sharing problem

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Cooperative positioning of UAV internet of things based on optimization algorithm

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WIRELESS NETWORKS 2024年第5期30卷 4495-4505页

作者： Hu, Hao Chen, YuLin Peng, Bao Li, Zhenjun Wu, Qibao Lin, ZiRan Wang, Xiaolei Bingtuan Xingxin Vocat & Tech Coll Urumqi 830023 Peoples R China South China Normal Univ Guangdong Prov Key Lab Opt Informat Mat & Technol South China Acad Adv Optoelect Guangzhou 510006 Peoples R China South China Normal Univ Inst Elect Paper Displays South China Acad Adv Optoelect Guangzhou 510006 Peoples R China Shenzhen Inst Informat Technol Shenzhen 518172 Peoples R China Shenzhen Inst Technol Shenzhen 518116 Peoples R China Guangdong Prov Engn Ctr China Made High Performan Shenzhen 518172 Peoples R China Tongji Univ Shanghai 200092 Peoples R China Shenzhen Polytech Shenzhen 518055 Peoples R China

Unmanned Aerial Vehicle (UAV) Internet of things have been widely used in military and civilian fields such as rescue, disaster relief, urban planning. Positioning service is the core technology for UAVs to perform various tasks. However, the UAV may be attacked by external conditions, resulting in its inability to obtain self-location information during mission. For the positioning problem of UAV signal interference, this paper proposes a cooperative positioning of UAV based on optimization algorithm. In order to solve the difficulty of UAV positioning, we propose the following solutions. Firstly, we construct different numbers of beacon nodes by using the flight information of UAVs in different cycles. Secondly, the unknown number of the positioning to be solved of the UAV is reduced to improve the accuracy and speed of the subsequent optimization algorithm. Thirdly, A multi-objective optimization model is established of the UAV motion parameters under inequality constraints. And we utilize a penalty function to convert the optimization model into a minimal value solution problem under no constraints. Finally, the positioning results of each UAV are obtained by the optimization algorithm.

关键词： UAV positioning Collaborative positioning Three-dimensional positioning optimization algorithm

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