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A metaheuristic algorithm for regulating virtual inertia of a standalone microgrid incorporating electric vehicles (vol 2024, e12383, 2024)

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JOURNAL OF ENGINEERING-JOE 2024年第6期2024卷

作者： Mishra, D. Maharana, M. K. Kar, M. K. Nayak, A. Islam, M. M. Ustun, T. S. KIIT Deemed be Univ Bhubaneswar Odisha India

Modern electrical networks, particularly microgrids, have seen a sharp rise in acquiring non‐conventional sources. The inertia of the microgrid decreases drastically because RESs have been used in place of traditional synchronous generators. The reduced inertia negatively impacts the dynamics and performance of the microgrid with RESs, which decreases the microgrid's stability, especially in operation on an island. The primary purpose of this research study is to enhance the dynamic security of an island microgrid by merging an electric vehicle with a frequency control technique based on virtual inertia control. A Proportional Integral Derivative Filter Constant (PIDFN) controller optimally created using the Modified Differential Evolution (MDE) method served as the foundation for control in the virtual inertia control loop. The effectiveness of the MDE‐based PIDFN controller was examined considering the diverse operational scenarios are compared and contrasted with those of traditional methods Differential Evolution (DE) and Teaching Learning Based Optimization (TLBO)‐based PIDFN controllers. Real‐time wind and solar power statistics and random load fluctuations were incorporated to provide realistic simulation settings. The outcomes demonstrate that the MDE‐based PIDFN controller performs better in reference frequency tracking and reducing frequency disturbances than the other optimization *** fluctuation due to RES intermittency, the proposed approach increases inertia and stability

关键词： power generation control distributed power generation synchronous generators electric vehicle charging Modified Differential Evolution method RESs island microgrid frequency control nonconventional sources modern electrical networks dynamic security electric vehicle Proportional Integral Derivative Filter Constant controller MDE-based PIDFN controller reducing frequency disturbances sharp rise virtual inertia control loop frequency control technique solar power stations reference frequency tracking diverse operational scenarios traditional synchronous generators power grids evolutionary computation metaheuristic algorithm standalone microgrid wind power plants

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Ebola Optimization Search algorithm: A New Nature-Inspired metaheuristic Optimization algorithm

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IEEE ACCESS 2022年 10卷 16150-16177页

作者： Oyelade, Olaide Nathaniel Ezugwu, Absalom El-Shamir Mohamed, Tehnan I. A. Abualigah, Laith Univ KwaZulu Natal Sch Math Stat & Comp Sci Pietermaritzburg Campus ZA-3201 Kwa Zulu South Africa Amman Arab Univ Fac Comp Sci & Informat Amman 11953 Jordan Univ Sains Malaysia Sch Comp Sci George Town 11800 Pulau Pinang Malaysia

Nature computing has evolved with exciting performance to solve complex real-world combinatorial optimization problems. These problems span across engineering, medical sciences, and sciences generally. The Ebola virus has a propagation strategy that allows individuals in a population to move among susceptible, infected, quarantined, hospitalized, recovered, and dead sub-population groups. Motivated by the effectiveness of this strategy of propagation of the disease, a new bio-inspired and population-based optimization algorithm is proposed. This study presents a novel metaheuristic algorithm named Ebola Optimization Search algorithm (EOSA) based on the propagation mechanism of the Ebola virus disease. First, we designed an improved SIR model of the disease, namely SEIR-HVQD: Susceptible (S), Exposed (E), Infected (I), Recovered (R), Hospitalized (H), Vaccinated (V), Quarantine (Q), and Death or Dead (D). Secondly, we represented the new model using a mathematical model based on a system of first-order differential equations. A combination of the propagation and mathematical models was adapted for developing the new metaheuristic algorithm. To evaluate the performance and capability of the proposed method in comparison with other optimization methods, two sets of benchmark functions consisting of forty-seven (47) classical and thirty (30) constrained IEEE-CEC benchmark functions were investigated. The results indicate that the performance of the proposed algorithm is competitive with other state-of-the-art optimization methods based on scalability, convergence, and sensitivity analyses. Extensive simulation results show that the EOSA outperforms popular metaheuristic algorithms such as the Particle Swarm Optimization algorithm (PSO), Genetic algorithm (GA), and Artificial Bee Colony algorithm (ABC). Also, the algorithm was applied to address the complex problem of selecting the best combination of convolutional neural network (CNN) hyperparameters in the image classi

关键词： Optimization Viruses (medical) Diseases metaheuristics Mathematical models Heuristic algorithms Statistics Ebola virus metaheuristic algorithm optimization problems constrained benchmark functions image classification convolutional neural network

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Puma optimizer (PO): a novel metaheuristic optimization algorithm and its application in machine learning

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2024年第4期27卷 5235-5283页

作者： Abdollahzadeh, Benyamin Khodadadi, Nima Barshandeh, Saeid Trojovsky, Pavel Gharehchopogh, Farhad Soleimanian El-kenawy, El-Sayed M. Abualigah, Laith Mirjalili, Seyedali Univ Hradec Kralove Fac Sci Dept Math Hradec Kralove 50003 Czech Republic Univ Miami Dept Civil & Architectural Engn Coral Gables FL 33146 USA Afagh Higher Educ Inst Sch Engn Dept Comp Sci Orumiyeh Iran Islamic Azad Univ Dept Comp Engn Urmia Branch Orumiyeh Iran Delta Higher Inst Engn & Technol Dept Commun & Elect Mansoura 35111 Egypt Al Ahliyya Amman Univ Hourani Ctr Appl Sci Res Amman 19328 Jordan Univ Tabuk Comp Sci Dept Dept Biol Tabuk 47913 Saudi Arabia Al al Bayt Univ Comp Sci Dept Mafraq 25113 Jordan Torrens Univ Australia Ctr Artificial Intelligence Res & Optimizat Adelaide Australia Obuda Univ Res & Innovat Ctr H-1034 Budapest Hungary

Optimization techniques, particularly meta-heuristic algorithms, are highly effective in optimizing and enhancing efficiency across diverse models and systems, renowned for their ability to attain optimal or near-optimal solutions within a reasonable timeframe. In this work, the Puma Optimizer (PO) is proposed as a new optimization algorithm inspired from the intelligence and life of Pumas in. In this algorithm, unique and powerful mechanisms have been proposed in each phase of exploration and exploitation, which has increased the algorithm's performance against all kinds of optimization problems. In addition, a new type of intelligent mechanism, which is a type of hyper-heuristic for phase change, is presented. Using this mechanism, the PO algorithm can perform a phase change operation during the optimization operation and balance both phases. Each phase is automatically adjusted to the nature of the problem. To evaluate the proposed algorithm, 23 standard functions and CEC2019 functions were used and compared with different types of optimization algorithms. Moreover, using the statistical test T-test and the execution time to solve the problem have been discussed. Finally, it has been tested using four machine learning and data mining problems, and the results obtained from all the analysis signifies the excellent performance of this algorithm against all kinds of problems compared to other optimizers. This algorithm has performed better than the compared algorithms in 27 benchmarks out of 33 benchmarks and has obtained better results in solving the clustering problem in 7 data sets out of 10 data sets. Furthermore, the results obtained in the problems of community detection and feature selection and MLP were superior. The source codes of the PO algorithm are publicly available at https://***/matlabcentral/fileexchange/157231-puma-optimizer-po.

关键词： Optimization metaheuristic algorithm Puma optimization algorithm Machine learning Global optimization Automatic phase change

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Gradient-based optimizer: A new metaheuristic optimization algorithm

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INFORMATION SCIENCES 2020年 540卷 131-159页

作者： Ahmadianfar, Iman Bozorg-Haddad, Omid Chu, Xuefeng Behbahan Khatam Alanbia Univ Technol Dept Civil Engn Behbahan Iran Univ Tehran Coll Agr & Nat Resources Fac Agr Engn & Technol Dept Irrigat & Reclamat Engn Tehran Iran North Dakota State Univ Dept Civil & Environm Engn Dept 2470 Fargo ND USA

In this study, a novel metaheuristic optimization algorithm, gradient-based optimizer (GBO) is proposed. The GBO, inspired by the gradient-based Newton's method, uses two main operators: gradient search rule (GSR) and local escaping operator (LEO) and a set of vectors to explore the search space. The GSR employs the gradient-based method to enhance the exploration tendency and accelerate the convergence rate to achieve better positions in the search space. The LEO enables the proposed GBO to escape from local optima. The performance of the new algorithm was evaluated in two phases. 28 mathematical test functions were first used to evaluate various characteristics of the GBO, and then six engineering problems were optimized by the GBO. In the first phase, the GBO was compared with five existing optimization algorithms, indicating that the GBO yielded very promising results due to its enhanced capabilities of exploration, exploitation, convergence, and effective avoidance of local optima. The second phase also demonstrated the superior performance of the GBO in solving complex real-world engineering problems. Source codes of the GBO algorithm are publicly available at http://***/codes/. (c) 2020 Elsevier Inc. All rights reserved.

关键词： Optimization Gradient-based method metaheuristic algorithm Constrained optimization problem

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Wolf-Bird Optimizer (WBO): A novel metaheuristic algorithm for Building Information Modeling-based resource tradeoff

Journal of Engineering Research (Kuwait)

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Journal of Engineering Research (Kuwait) 2024年

作者： Azizi, Mahdi Baghalzadeh Shishehgarkhaneh, Milad Basiri, Mahla Moehler, Robert C. Fang, Yihai Chan, Melissa Department of Civil Engineering University of Tabriz Iran Department of Civil Engineering Monash University Clayton 3800 VIC Australia Department of Infrastructure Engineering The University of Melbourne Melbourne Australia Institute for Sustainable Industries & Liveable Cities (ISILC) Victoria University Melbourne 3011 Australia College of Sport Health and Engineering Victoria University Melbourne 3011 Australia

In the animal kingdom, a mutually-beneficial ecosystemic coexistence and partnership in predation between wolves and ravens, known as the wolf-bird relationship, is observed in various cultures. The Wolf-Bird Optimizer (WBO), a novel metaheuristic algorithm inspired by this natural zoological relationship, is proposed. This method is developed based on the foraging behaviors of ravens and wolves, wherein the intelligence of ravens in finding prey and sending signals to wolves for assistance in hunting is considered. Furthermore, a framework for resource tradeoffs in project scheduling using metaheuristic algorithms and the Building Information Modeling (BIM) approach is established in this research. For statistical analysis, the algorithms are independently run 30 times with a preset stopping condition, enabling the calculation of descriptive statistical metrics such as mean, standard deviation (SD), and the required number of objective function evaluations. To ensure the statistical significance of the results, several inferential statistical methods, including the Kolmogorov-Smirnov, Wilcoxon, Mann-Whitney, and Kruskal-Wallis tests, are employed. Additionally, the capability of the proposed algorithm in solving resource tradeoff problems in four construction projects is assessed. The performance of the WBO algorithm is also evaluated in two benchmark construction projects, with the results indicating the algorithm's ability to produce competitive and exceptional outcomes regarding tradeoffs. © 2023 The Authors

关键词： Bioinspiration Evolutionary computation, Building information modeling metaheuristic algorithm Resource management Wolf-bird optimizer

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metaheuristic Optimization algorithm for Day-Ahead Energy Resource Management (ERM) in Microgrid Environment of Power System

Metaheuristic Optimization Algorithm for Day-Ahead Energy Re...

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International Conference on Power, Control and Communication Infrastructure (ICPCCI)

作者： Dabhi, Dharmesh Pandya, Kartik CHARUSAT Univ M&V Patel Dept Elect Engn CSPIT Changa 388421 Gujarat India

ISBN: (纸本)9789811509742;9789811509735

The day-ahead Energy Resource Management (ERM) problem with the aim to backing the functioning decisions of Virtual Power Player (VPP) in the microgrid environment. The aim of the VPP is to manage the available distributed energy resources as practically as possible with the objective of minimizing the operational cost and maximizing profits by reducing the need to buy energy from the external supplier or electricity market at high prices. The day-ahead ERM is executed the day before the energy trades are due. Typically, the considered trades periods are one-hour corresponding to 24 scheduling periods. A vital input to the ERM is each hour forecasting demand, which can be done using correct forecasting methods. VPP can aggregate the all types of energy resources like, DGs, PV, electric vehicles, energy storage, demand response and electricity market. The use of Vehicle to Grid (or G2V), PV, and energy storage technology can help to increase the penetration of non dispatchable uncertain renewable based DGs. The drawback of large DERs penetration is that the optimal scheduling problem turns into a complex optimization problem and becomes hard to be addressed by deterministic techniques, because these techniques can take a large execution time for obtaining the optimal solution. On the other hand, the VPP has its own optimal scheduling related time constraints. For these reasons, metaheuristic techniques are very useful to support the VPP in the computation of a good solution with a low execution time. This paper proposed the new metaheuristic algorithm called Cross-Entropy Variable Neighborhood Differential Evolutionary Particle Swarm Optimization (CE-VNDEPSO) for addressing the Energy Resource Management (ERM) problem of 25-bus microgrid systems. The effectiveness of CE-VNDEPSO algorithm is finding out by comparing its performance with the well-known optimization algorithms like, Variable Neighborhood Search (VNS), Differential Evolutionary Particle Swarm Optimizatio

关键词： Virtual power player (VPP) Cross-Entropy variable neighborhood differential evolutionary particle swarm optimization (CE-VNDEPSO) Distributed energy resources (DER) Energy resource management (ERM) metaheuristic algorithm

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Circular Structures of Puffer Fish: A New metaheuristic Optimization algorithm 3

Circular Structures of Puffer Fish: A New Metaheuristic Opti...

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3rd International Conference on Electrical and Biomedical Engineering, Clean Energy and Green Computing (EBECEGC)

作者： Catalbas, Mehmet Cem Gulten, Arif Firat Univ Dept Elect & Elect Engn Elazig Turkey

ISBN: (纸本)9781538627426

In this paper, we presented a new metaheuristic optimization algorithm which we named as circular structures of puffer fish. This algorithm has been inspired by the behaviors of male puffer fish for charming the female ones. The special type of male puffer fishes, Torquigener albomaculosus, has a special ability to build a unique circular structure on the seabed. The building process of a circular structure consists of several regular sub-dimensions and as the result of this process, a circular structure is constructed. The water flow rate at the center of this structure is reduced. Additionally, these structures have some numerical properties such as the ratio of inner and outer circular structures diameter or the number of peaks on the structure. The results of the proposed algorithm are compared with genetic algorithm through various performance criteria. The most popular performance analyzing functions were used in the optimization of the algorithm performance comparison process.

关键词： component optimization problem metaheuristic algorithm bio-inspired algorithms genetic algorithm adaptive search

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WOAGA: A new metaheuristic mapping algorithm for large-scale mesh-based NoC

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IEICE ELECTRONICS EXPRESS 2018年第17期15卷 20180738-20180738页

作者： Wang, Xilu Sun, Yongjun Gu, Huaxi Liu, Zujun Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Shaanxi Peoples R China

The mapping of IP cores to the topology is one of the most important steps for NoC (Network-on-Chip) design. metaheuristic algorithms (MAs) are widely employed since the mapping is an NP-hard problem. Most mapping algorithms only consider small-scale NoC and ignore stability. In this letter, a stable metaheuristic algorithm called WOAGA, based on Whale Optimization algorithm (WOA) and Genetic algorithm (GA), is proposed for large-scale NoC mapping to achieve the low-energy consumption and stability. In the proposed algorithm, irregular crossover and mutation operations are integrated into the modified WOA. A perturbation is utilized to jump out of local optima effectively. Simulation results show that the proposed algorithm is more stable and achieve better solution with energy consumption reduced significantly.

关键词： network-on-chip mapping metaheuristic algorithm

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Forecasting wind power based on an improved al-Biruni Earth radius metaheuristic optimization algorithm

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FRONTIERS IN ENERGY RESEARCH 2023年 11卷

作者： Saeed, Mohammed A. Ibrahim, Abdelhameed El-Kenawy, El-Sayed M. Abdelhamid, Abdelaziz A. El-Said, M. Abualigah, Laith Alharbi, Amal H. Khafaga, Doaa Sami Elbaksawi, Osama Mansoura Univ Fac Engn Elect Engn Dept Mansoura Egypt Mustaqbal Univ Coll Engn & Comp Sci Comp Engn Dept Buraydah Saudi Arabia Delta Higher Inst Engn & Technol Dept Commun & Elect Mansoura Egypt Shaqra Univ Coll Comp & Informat Technol Dept Comp Sci Shaqra Saudi Arabia Ain Shams Univ Fac Comp & Informat Sci Dept Comp Sci Cairo Egypt Delta Higher Inst Engn & Technol Mansoura Egypt Al Ahliyya Amman Univ Hourani Ctr Appl Sci Res Amman Jordan Middle East Univ MEU Res Unit Amman Jordan Univ Sains Malaysia Sch Comp Sci George Town Malaysia Al Al Bayt Univ Prince Hussein Bin Abdullah Fac Informat Technol Comp Sci Dept Mafraq Jordan Appl Sci Private Univ Appl Sci Res Ctr Amman Jordan Princess Nourah Bint Abdulrahman Univ Coll Comp & Informat Sci Dept Comp Sci Riyadh Saudi Arabia Port Said Univ Fac Engn Elect Engn Dept Port Said Egypt

Wind power forecasting is pivotal in optimizing renewable energy generation and grid stability. This paper presents a groundbreaking optimization algorithm to enhance wind power forecasting through an improved al-Biruni Earth radius (BER) metaheuristic optimization algorithm. The BER algorithm, based on stochastic fractal search (SFS) principles, has been refined and optimized to achieve superior accuracy in wind power prediction. The proposed algorithm is denoted by BERSFS and is used in an ensemble model's feature selection and optimization to boost prediction accuracy. In the experiments, the first scenario covers the proposed binary BERSFS algorithm's feature selection capabilities for the dataset under test, while the second scenario demonstrates the algorithm's regression capabilities. The BERSFS algorithm is investigated and compared to state-of-the-art algorithms of BER, SFS, particle swarm optimization, gray wolf optimizer, and whale optimization algorithm. The proposed optimizing ensemble BERSFS-based model is also compared to the basic models of long short-term memory, bidirectional long short-term memory, gated recurrent unit, and the k-nearest neighbor ensemble model. The statistical investigation utilized Wilcoxon's rank-sum and analysis of variance tests to investigate the robustness of the created BERSFS-based model. The achieved results and analysis confirm the effectiveness and superiority of the proposed approach in wind power forecasting.

关键词： forecasting wind power al-Biruni Earth radius metaheuristic algorithm artificial intelligence optimization

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Synthesis and Comparison of Improved Edge Detection Technique Based on metaheuristic and Intelligent algorithm Optimization

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TRAITEMENT DU SIGNAL 2021年第6期38卷 1613-1622页

作者： Moussa, Mourad Douik, Ali Univ Sousse Ecole Natl Ingn Sousse ENISO NOCCS Lab Pole Technol Sousse Route Ceinture Sahloul Sousse 4054 Tunisia

The edge is the most significant information in image processing applications. Moreover, and the accurate and continue edge commonly leads to accurate related steps like object tracking and region clustering. In fact, it is the first step of image analysis and understanding. The accuracy edge detection results have an impact on the comprehension machine system. In this paper we present various improved edge detection techniques by our research team, of similar color and grey level images, using the information theory approach based on other energy information inspired from Shannon entropy and utilizing as well the metaheuristic and intelligent method combined with multilevel thresholding approach in various color spaces, and like the ant colony optimization with the graph cut approach for indexing images before the segmentation step. In addition, particular swarm optimization is done, and finally the fuzzy technique is used. The effectiveness and accuracy of these approaches are evaluated by many metric measurements and compared with the common operators. The PR metric, has a significant mean value (about 20) than PR of Canny operator (about 9). And also, we can denote that all improved techniques achieve significant results with ameliorated running time.

关键词： neural network Shannon entropy joint entropy fuzzy logic conditional entropy metaheuristic algorithm

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