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metaheuristic algorithm-Based Optimal Energy Operation Scheduling and Energy System Sizing Scheme for PV-ESS Integrated Systems in South Korea

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INTERNATIONAL JOURNAL OF ENERGY RESEARCH 2024年第1期2024卷

作者： Park, Sungwoo Oh, Jinyeong Hwang, Eenjun Korea Univ Sch Elect & Elect Engn Seoul South Korea

To efficiently utilize the power generated by a photovoltaic (PV) system, integrating it with an energy storage system (ESS) is essential. Furthermore, maximizing the economic benefits of such PV-ESS integrated systems requires selecting the optimal capacity and performing optimal energy operation scheduling. Although many studies rely on rule-based energy operation scheduling, these methods prove inadequate for complex real-world scenarios. Moreover, they often focus solely on determining the ESS capacity to integrate into existing PV systems, thereby limiting the possibility of achieving optimal economic benefits. To address this issue, we propose an optimal energy operation scheduling and system sizing scheme for a PV-ESS integrated system based on metaheuristic algorithms. The proposed scheme employs a zero-shot PV power forecasting model to estimate the potential power generation from a planned PV system. A systematic analysis of the installation, operation, and maintenance costs is then incorporated into the economic analysis. We conducted extensive experiments for comparing economic benefits of various scheduling methods and capacities using real electrical load data collected from a private university in South Korea and estimated PV power data. According to the results, the most effective metaheuristic algorithm for scheduling is simulated annealing (SA). Additionally, the optimal PV system, battery, and power conversion system capacities for the university are 13,000 kW each, 10% of the PV system capacity, and 60% of the battery capacity, respectively. The estimated annual electricity tariff calculated from the data used in the experiment is $3,315,484. In contrast, SA-based scheduling in the optimal PV-ESS integrated system achieved annual economic benefits of $875,000, an improvement of approximately 7% over rule-based scheduling of $817,730.

关键词： energy operation scheduling energy storage system energy system sizing metaheuristic algorithm photovoltaic system zero-shot photovoltaic power forecasting

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A novel metaheuristic algorithm: advanced social memory optimization

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PHYSICA SCRIPTA 2025年第5期100卷 055004-055004页

作者： Fan, Shijie Wang, Ruichen Su, Kang Shijiazhuang Tiedao Univ Sch Mech Engn Shijiazhuang 050043 Peoples R China

The field of optimization problems has garnered significant attention due to its importance across various applications, particularly driven by the demand for efficient solutions to complex engineering challenges. Numerous metaheuristic algorithms inspired by animal behaviour or swarm intelligence have been proposed;however, these algorithms often pursue a multitude of strategies, resulting in excessive parameters that complicate tuning and hinder convergence and balance. Additionally, algorithms based on human behaviour remain scarce. To address these limitations, extensive research has been conducted on the interplay between social memory and individual memory, leading to the introduction of a novel human-behaviour-inspired metaheuristic algorithm, named Advanced Social Memory Optimization (ASMO). This algorithm seeks to address the complexities of parameter management, convergence, and balance more effectively with a streamlined set of strategies. Furthermore, a mathematical model based on the mechanisms of social memory formation and individual memory updating underpins the algorithm. Rigorous performance evaluations, utilizing the Wilcoxon Rank-Sum Test and the Friedman Test across multiple benchmark suites (CEC2017, CEC2019, and CEC2022), demonstrate that ASMO, with only two algorithmic strategies, outperforms or matches established algorithms on more than half of the test functions. These findings suggest promising new avenues for research in the field of optimization and, given the succinctness of ASMO's strategies, underscore its potential as a powerful tool for enhancing and developing solutions to complex engineering design problems. The code for the ASMO algorithm is available in appendix D.

关键词： advanced social memory optimization social memory individual memory engineering applications metaheuristic algorithm

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A metaheuristic algorithm for efficient aircraft sequencing and scheduling in terminal maneuvering areas

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OPTIMIZATION LETTERS 2025年第3期19卷 579-604页

作者： Gui, Dongdong Le, Meilong Luo, Xiaodong Huang, Zhouchun Nanjing Univ Aeronaut & Astronaut Coll Civil Aviat Nanjing Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Econ & Management Nanjing Peoples R China Shenzhen Res Inst Big Data Shenzhen Peoples R China

Air traffic controllers face a daily challenge ensuring the safe and orderly flow of aircraft in terminal maneuvering areas, known academically as the Aircraft Sequencing and Scheduling Problem (ASSP). Typically formulated as a mixed integer linear programming problem, the strong NP-hard nature of the ASSP necessitates the use of heuristics or metaheuristics for practical, time-efficient solutions. In this paper, we propose a new metaheuristic algorithm to address the computational complexity of the problem. The algorithm employs a single-solution-based search methodology that starts from a well-constructed initial solution and iteratively explores a fix-and-optimize procedure to find an improved solution. Specifically, this procedure solves a variant of the ASSP that contains all the continuous variables and a selected subset of binary variables of the original problem, with the remaining binary variables being temporarily fixed to the values determined based on previous iterations. Additionally, the algorithm incorporates tabu search to increase the possibility of quickly finding optimal solutions. A rolling horizon control approach further accelerates the solution derivation process by decomposing the original problem into smaller subproblems. Our computational experiments using real traffic data from Guangzhou Baiyun International Airport demonstrates the effectiveness of the algorithm in terms of solution quality and computational time.

关键词： Aircraft sequencing and scheduling metaheuristic algorithm Mixed integer linear programming Fix-and-optimize

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Virus diffusion algorithm: a novel metaheuristic algorithm for segmenting the spun crack

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MEASUREMENT SCIENCE AND TECHNOLOGY 2025年第4期36卷 045601-045601页

作者： Wan, Miao Lin, Yong-Cheng Chen, Ming-Song Zeng, Ning-Fu Wu, Gui-Cheng Zhang, Hui-Jie Cent South Univ Sch Mech & Elect Engn Changsha 410083 Peoples R China State Key Lab Precis Mfg Extreme Serv Performance Changsha 410083 Peoples R China Rongcheng Huadong Met Forming Machinery Co Ltd Rongcheng 264300 Peoples R China

metaheuristic algorithms are extensively utilized in engineering due to their outstanding capacity for solving optimization problems with restricted computing resources or incomplete data. However, its extended use is constrained by the low optimization accuracy and premature convergence. The rapid spread and extensive reach of the COVID-19 virus have inspired the proposal of a new virus diffusion algorithm (VDA) to overcome the limitations of the metaheuristic algorithm. This article utilizes the VDA algorithm to segment spun cracks, providing a method for intelligent detection of spinning process. The algorithm integrates global diffusion and local diffusion mechanisms to simulate both the random walk and local disturbance modes of virus diffusion, thereby enhancing its accuracy. Additionally, it introduces the competition mechanism and infection center rate to enhance the diversity of the population and expand the algorithm's search range. The effectiveness and robustness of the VDA algorithm is validated using the CEC'17 test benchmark function. Subsequently, the VDA algorithm is used to segment images with cracks in thin-walled spun parts. The experimentally obtained results illustrate that the VDA-based segmentation algorithm attains a PSNR of 23.6798 and an SSIM of 0.9864 for crack images, surpassing other segmentation algorithms in challenging conditions.

关键词： metaheuristic algorithm machine vision spinning detection image segmentation intelligent monitoring

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Leaf in Wind Optimization: A New metaheuristic algorithm for Solving Optimization Problems

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IEEE ACCESS 2024年 12卷 56291-56308页

作者： Fang, Ning Cao, Qi Beihang Univ Sch Elect & Informat Engn Beijing 100191 Peoples R China Longyan Tobacco Ind Co Ltd Longyan 364021 Peoples R China

This study introduces a novel metaheuristic algorithm, Leaf in Wind Optimization, inspired by the natural phenomenon of falling leaves in the wind. The proposed method simulates the motion response of leaves in varying intensities of wind by establishing models for light wind-driven blades and strong wind-driven blades. The algorithm incorporates motion modes of linear translation and spiral rotation induced by wind, offering a hybrid search framework suitable for both strategies. This approach enables enhanced exploration and exploitation of the search space. The algorithm's performance was evaluated using three challenging benchmark test sets, CEC 2017, CEC 2019 and CEC 2022, as well as an engineering practical problem. Its effectiveness was assessed through comparison with 10 random optimization algorithms, namely: Tree Seed algorithm, Multi-Verse Optimizer, Salp Swarm algorithm, Artificial Ecosystem-based Optimization, Hunger Games Search, Fox Optimizer, Spider Wasp Optimizer, AOBLMOA, Enhanced Snake Optimizer, and IbI Logic algorithm. In the comprehensive testing conducted, the proposed algorithm consistently outperformed other optimizers in approximately 82% of comparisons. Through examination of convergence curves and statistical data, it is evident that Leaf in Wind Optimization demonstrates superior potential compared to the alternative optimizers under consideration.

关键词： Vegetation metaheuristics Approximation algorithms Wind Heuristic algorithms Particle swarm optimization Mathematical models metaheuristic algorithm optimization benchmark functions

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From constraints fusion to manifold optimization: A new directional transport manifold metaheuristic algorithm

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INFORMATION FUSION 2025年 113卷

作者： Snasel, Vaclav Kong, Lingping Das, Swagatam VSB Tech Univ Ostrava Dept Comp Sci Listpodu 17 Ostrava Czech Republic Indian Stat Inst Elect & Commun Sci Unit Kolkata 700108 India

The ascent of geometry-based models and methodologies, exemplified by geometric deep learning and manifold numerical optimization algorithms, has inaugurated a novel domain across various applications that grapple with geometric data complexities, such as electroencephalogram signals represented by symmetric positive definite matrix manifold, hierarchical data represented by hyperbolic manifold. The imperative fuels this inevitable paradigm shift to encapsulate the intricacies and richness inherent in data, areas where traditional methods prove inadequate. While metaheuristic algorithms are renowned for their versatile adaptability across applications, offering practical solutions within reasonable timeframes. However, the conventional metaheuristic algorithms fail on manifold applications with meaningless solutions. From an extrinsic optimization perspective, we treat manifold optimization problems as general optimization problems with multiple fused constraints that limit the optimization path to the manifold. This study pioneered the proposal and implementation of a metaheuristic manifold optimization, introducing a novel directional transport operator to rectify previously identified issues. Through experimentation across five sets of 25 problems, comparing against five algorithms, including both gradient-free and gradient-dominant counterparts, our proposed algorithm emerges as the optimal performer within the gradient-free category, demonstrating competitiveness even against gradient-dominant algorithms. Furthermore, we applied the proposed algorithm to the robot dynamic manipulation problem, achieving a close-optimal solution that eludes gradient-dominant approaches. This paper delves into the inherent capabilities and establishes the generalization of a metaheuristic algorithm within non-Euclidean functional landscapes. The source code will be available at https://***/lingpingfuzzy/metaheuristic-manifold-optimization.

关键词： Optimization on manifolds Matrix manifolds metaheuristic algorithm Extrinsic manifold Intrinsic manifold

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Advances in Henry Gas Solubility Optimization: A Physics-Inspired metaheuristic algorithm With Its Variants and Applications

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IEEE ACCESS 2024年 12卷 26062-26095页

作者： El-Shorbagy, Mohammed A. Bouaouda, Anas Nabwey, Hossam A. Abualigah, Laith Hashim, Fatma A. Prince Sattam bin Abdulaziz Univ Coll Sci & Humanities Al Kharj Dept Math Al Kharj 11942 Saudi Arabia Hassan II Univ Casablanca Fac Sci & Technol Mohammadia 28806 Morocco Menoufia Univ Fac Engn Dept Basic Engn Sci Shibin Al Kawm 32511 Egypt Univ Tabuk Artificial Intelligence & Sensing Technol AIST Res Tabuk 71491 Saudi Arabia Al Ahliyya Amman Univ Hourani Ctr Appl Sci Res Amman 19328 Jordan Middle East Univ MEU Res Unit Amman 11831 Jordan Lebanese Amer Univ Dept Elect & Comp Engn Byblos 135053 Lebanon Isra Univ Fac Informat Technol Amman 11622 Jordan Yuan Ze Univ Coll Engn Taoyuan 32003 Taiwan Appl Sci Private Univ Appl Sci Res Ctr Amman 11931 Jordan Helwan Univ Fac Engn Cairo 11795 Egypt

The Henry Gas Solubility Optimization (HGSO) is a physics-based metaheuristic inspired by Henry's law, which describes the solubility of the gas in a liquid under specific pressure conditions. Since its introduction by Hashim et al. in 2019, HGSO has gained significant attention for its unique features, including minimal adaptive parameters and a balanced exploration-exploitation trade-off, leading to favorable convergence. This study provides an up-to-date survey of HGSO, covering the walk through the historical development of HGSO, its modifications, and hybridizations with other algorithms, showcasing its adaptability and potential for synergy. Recent variants of HGSO are categorized into modified, hybridized, and multi-objective versions, and the review explores its main applications, demonstrating its effectiveness in solving complex problems. The evaluation includes a discussion of the algorithm's strengths and weaknesses. This comprehensive review, featuring graphical and tabular comparisons, not only indicates potential future directions in the field but also serves as a valuable resource for researchers seeking a deep understanding of HGSO and its advanced versions. As physics-based metaheuristic algorithms gain prominence for solving intricate optimization problems, this study provides insights into the adaptability and applications of HGSO across diverse domains.

关键词： Heuristic algorithms metaheuristics Search problems Behavioral sciences Mathematical models Task analysis Space exploration Problem-solving Engineering - general Engineering problems global optimization Henry gas solubility optimization metaheuristic algorithm

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Application of metaheuristic algorithm in intelligent logistics scheduling and environmental sustainability

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INTELLIGENT DECISION TECHNOLOGIES-NETHERLANDS 2024年第3期18卷 1727-1740页

作者： Jin, Xin Kunming Univ Sch Econ & Management Kunming Yunnan Peoples R China

In response to the problems of low efficiency, high cost, and serious environmental pollution faced by traditional logistics scheduling methods, this article introduced the metaheuristic algorithm into intelligent logistics scheduling and environmentally sustainable development. This article took the metaheuristic algorithm as the research object. It was based on an in-depth analysis of its core ideas and unique advantages, combined intelligent logistics scheduling with relevant theories and methods such as green environmental protection, and innovatively constructed an intelligent logistics scheduling model based on the metaheuristic algorithm. This article experimentally compared the effects of different metaheuristic algorithms on total driving distance, transportation time, fuel consumption, and carbon emissions. The experimental findings indicated that the ant colony optimization (ACO) algorithm in this article performed the best among them, and the performance of traditional algorithms and metaheuristic algorithms was also tested in terms of performance. The findings indicated that the computational accuracy of the metaheuristic algorithm reached 97%, which was better than the traditional 80%. Experimental results have shown that the metaheuristic algorithm is an efficient and feasible method that can improve the efficiency of logistics scheduling and environmental sustainability.

关键词： metaheuristic algorithm intelligent logistics scheduling environmental sustainability path optimization performance test

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A new improved Newton metaheuristic algorithm for solving mathematical and structural optimization problems

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EVOLUTIONARY INTELLIGENCE 2024年第4期17卷 2749-2789页

作者： Amiri, Ahmad Torkzadeh, Peyman Salajegheh, Eysa Shahid Bahonar Univ Kerman Dept Civil Engn Kerman Iran

As the objective function in complex structural optimization problems is implicit, in the present improved Newton method using the higher-order approximation, a new and simple formulation is proposed to calculate the first and second-order derivatives of the objective function. Furthermore, by combining the presented improved Newton method with an innovative metaheuristic algorithm an efficient approach, called improved Newton metaheuristic algorithm (INMA), is presented to balance between exploration and exploitation during optimization process. The performance of the proposed INMA algorithm is investigated on the CEC2019 and CEC2020 benchmark functions, three benchmark truss problems with stress constraints as well as four truss problems with multiple frequency constraints. The results demonstrate that the efficiency of the proposed INMA algorithm for both mathematical and structural optimization problems is better than other algorithms. Anyway, it can be seen from the results that INMA algorithm is an efficient method to solve problems in different optimization domains.

关键词： metaheuristic algorithm Improved Newton method Structural optimization Truss problem

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Literature Research Optimizer: A New Human-Based metaheuristic algorithm for Optimization Problems

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2024年第9期49卷 12817-12865页

作者： Ni, Lei Ping, Yan Yao, Na Jiao, Jiao Wang, Geng Southwest Univ Sci & Technol Key Lab Testing Technol Mfg Proc Minist Educ Mianyang 621010 Peoples R China Southwest Univ Sci & Technol Tianfu Inst Res & Innovat Chengdu 610299 Peoples R China Univ Elect Sci & Technol China Sch Aeronaut & Astronaut Chengdu 610054 Peoples R China

Traditional deterministic optimization algorithms are difficult to effectively solve many real-world nonlinear, complex, and high-dimensional optimization problems. The metaheuristic optimization algorithm has evolved in recent years into a kind of very popular algorithm due to its gradient-free and random nature. In this paper, a new metaheuristic algorithm, namely the literature research optimization algorithm (LRO), is proposed. In this algorithm, the literature research process is represented as a mathematical model, and new mechanisms are designed to help realize the global exploration and local exploitation. To evaluate the performance of LRO, three sets of test functions including the standard benchmark set, CEC2017, and CEC2019 are applied first, and then, six engineering problems are employed to further verify it. The Friedman test and Wilcoxon rank sum test are also used to statistically compare the proposed LRO algorithm with ten common existing metaheuristics. The results show that the LRO algorithm outperforms the comparison algorithms on most benchmark functions and that it also performs well in practical engineering applications. The LRO algorithm can provide superior solutions to most optimization problems compared to other metaheuristic algorithms and become a promising candidate for many real-life optimization problems.

关键词： Literature research optimizer metaheuristic algorithm Human-based Optimization Real-world problems

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