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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 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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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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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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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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Griffon vultures optimization algorithm for solving optimization problems

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 276卷

作者： Hasan, Dler O. Mohammed, Hardi M. Abdul, Zrar Khalid Charmo Univ Coll Sci Dept Comp Sci Sulaimani 46023 Kurdistan Regio Iraq

This study presents the Griffon Vultures optimization algorithm (GVOA), a novel bio-inspired metaheuristic based on the intelligent group foraging behaviors of griffon vultures in their natural environment. The GVOA integrates four primary foraging strategies: following behavior, group foraging, independent search, and nearcarcass scouting, enabling an effective balance between exploration and exploitation throughout the optimization process. By mathematically modeling the dynamic patterns and behaviors of griffon vultures, this study develops a robust global optimization algorithm. The performance of GVOA is evaluated against 21 recent algorithms across various test sets, including 23 classical benchmark functions, 30 CEC2017 benchmark functions (in 10, and 30 dimensions), 10 CEC2019 benchmarks, and 12 CEC2022 benchmarks. Experimental findings reveal that GVOA surpasses competitors on 91 % of the classical functions, 100% of the CEC2019 suite, and 58% of the CEC2022 suite. Additionally, it achieves superior performance on 70 % of the CEC2017 suite for both 10and 30-dimensional problems. Furthermore, the algorithm's applicability is further assessed through seven engineering benchmarks and a practical engineering application, underscoring its effectiveness in addressing complex real-world challenges. Overall, GVOA exhibits exceptional capabilities in exploration, exploitation, and maintaining a balance between the two while effectively avoiding local optima. It proves to be highly competitive, particularly in optimization tasks characterized by unimodal properties, multiple constraints, and numerous variables.

关键词： GVOA Metaheuristic optimization algorithm exploration and exploitation Engineering problems Griffon vultures

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An energy-aware technique for solving the offloading problem of mobile internet of things using an enhanced neural network-based optimization algorithm

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2025年第4期28卷 1-16页

作者： Xia, Haoji Cao, Jinyan Univ Glasgow Univ Ave Glasgow City G12 8QQ Scotland Taiyuan Univ Technol Coll Elect & Power Engn Taiyuan 030024 Shanxi Peoples R China

Internet of Things (IoT) is the connection of devices to the Internet, where devices such as cameras, sensors, light bulbs, and smoke alarms are able to communicate with each other and their users. IoT allows devices to assist daily routines, such as cars, which can be synced with calendars for appointment or meeting tracking to plan the best routes. The IoT brings new potential to humans' everyday life activities by enabling applications with stringent communication demands, including Mobile IoT (MIoT) applications. However, since most apps in MIoT have quality of service demands, running them on mobile devices with limited resources raises several difficulties. Offloading the part of the apps' services to nearby substitute devices is one way to handle increasingly demanding applications. Also, with the increasing demands of big data applications in the IoT, various applications have emerged to provide mobile services for IoT devices. However, due to the increasing number of IoT devices, transmission delay, energy consumption, and heavy load have become new challenges for offloading tasks. Therefore, this article introduces an energy-aware task offloading method in MIoT to determine the optimal offloading strategy. Due to the NP-hard nature of this problem and to enhance the efficiency of the state-of-the-art techniques, this paper used a neural network-based evolutionary optimization algorithm. The usage of multi-layer perceptron neural networks with an error back-propagation training method is one of the effective solutions in this paper for enhancing load-discharge performance. The potential for trapping at local minimum points is one of the flaws of the error back-propagation training process. Meta-heuristic algorithms are able to get out of the trap of local minima and find global minima. Thus, in this paper, the combination of the locust search optimization algorithm with the error back-propagation training algorithm has been used to eliminate the weakness of

关键词： Task offloading Neural network Mobile Internet of things Energy optimization algorithm

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Practice of an improved many-objective route optimization algorithm in a multimodal transportation case under uncertain demand

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COMPLEX & INTELLIGENT SYSTEMS 2025年第2期11卷 1-22页

作者： Cui, Tianxu Shi, Ying Wang, Jingkun Ding, Rijia Li, Jinze Li, Kai China Univ Min & Technol Beijing Sch Management Beijing 100083 Peoples R China Beijing Wuzi Univ Sch Informat Beijing 101149 Peoples R China

In recent decades, multimodal transportation has played a crucial role in modern logistics and transportation systems because of its high capacity and low cost. However, multimodal transportation driven mainly by fossil fuels may result in significant carbon emissions. In addition, transportation costs, transportation efficiency, and customer demand are also key factors that constrain the development of multimodal transportation. In this paper, we develop, for the first time, a many-objective multimodal transportation route optimization (MTRO) model that simultaneously considers economic cost, carbon emission cost, time cost, and customer satisfaction, and we solve it via the nondominated sorting genetic algorithm version III (NSGA-III). Second, to further improve the convergence performance, we introduce a fuzzy decision variable framework to improve the NSGA-III algorithm. This framework can reduce the search range of the optimization algorithm in the decision space and make it converge better. Finally, we conduct numerous simulation experiments on test problems to verify the applicability and superiority of the improved algorithm and apply it to MTRO problems under uncertain demand. This work fills the research gap for MTRO problems and provides guidance for relevant departments in developing transportation and decarbonization plans.

关键词： Green logistics Carbon emissions Multimodal transportation Many-objective optimization algorithm

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Design-manufacturing-evaluation integrated of microwave absorption metastructure based on large variation genetic-ant colony fusion optimization algorithm and additive manufacturing

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COMPOSITES COMMUNICATIONS 2025年 54卷

作者： Huang, Yixing Zhao, Pengzhen Sun, Yingjian Liao, Haitao Dong, Huaiyu Gao, Shuailong Li, Qun Zhao, Tian Li, Ying Beijing Inst Technol Inst Adv Struct Technol Beijing Key Lab Lightweight Multifunct Composite M Beijing 100081 Peoples R China Shandong Univ Aeronaut Sch Chem Engn & Safety Binzhou 256600 Peoples R China

Absorbing materials and structures are widely used in military and civilian fields, such as electromagnetic wave protection and stealth technology. However, current materials face challenges including high mass, limited loadbearing capacity, and the lack of integrated material-structure designs. In this study, we combined additive manufacturing technology with a hybrid optimization algorithm, integrating large mutation genetic and ant colony algorithms, to develop a novel lightweight honeycomb structure. The optimized structure achieved broadband absorption over 91.84 % of the 2 GHz-40 GHz range and demonstrated superior mechanical performance, with a maximum deflection of 13.72 mm and a load capacity of 2.87 kN. These findings present an innovative strategy for designing lightweight structures that effectively balance electromagnetic absorption and mechanical durability.

关键词： Design-manufacturing-evaluation integrated Large variation genetic and ant colony fusion optimization algorithm Microwave absorbing metastructure

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Early Warning of College Students' Ideological and Political Course Performance Using an optimization algorithm

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JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS 2025年第2期29卷 389-395页

作者： Chen, Yuehua Hebei Chem & Pharmaceut Coll Dept Marxism Teaching 88 Fangxing Rd Shijiazhuang 050026 Peoples R China

With the reform of teaching methods, hybrid online and offline teaching modes have been used increasingly in college courses. In this setting, the factors affecting academic performance are more complex, making it more challenging to predict students' performance. Therefore, there is an urgent need for higher- performance prediction algorithms. This study briefly analyzed college students' learning in ideological and political courses. Then, the learning features of college students in the courses were extracted using the Super Star platform and teaching system. Feature selection was carried out based on the information gain rate, while the training set was balanced using the synthetic minority oversampling technique (SMOTE). Moreover, the seagull optimization algorithm (SOA) was applied to optimize the hyperparameters of eXtreme Gradient Boosting (XGBoost) to develop the SOA-XGBoost algorithm for early warning of performance. Experiments were performed on the collected datasets. It was found that the effect of the SOA-XGBoost algorithm on the early warning of performance improved significantly following SMOTE processing. The F1- value reached 0.955 and the area under the curve value was 0.976. The SOA exhibited superior performance in hyperparameter optimization compared with other algorithms such as the grid search. The SOA-XGBoost algorithm also achieved the best results in early warning of performance. These results confirm the effectiveness of the proposed SOA-XGBoost algorithm for early warning of performance, and the method can be widely applied in practice.

关键词： ideological and political course performance warning optimization algorithm XGBoost

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