A Novel Improved Gradient‐Based Optimizer for Single‐Sensor Global MPPT of PV System DOI Creative Commons
Hegazy Rezk, Usama Hamed Issa, Anas Bouaouda

et al.

Journal of Mathematics, Journal Year: 2025, Volume and Issue: 2025(1)

Published: Jan. 1, 2025

Gradient‐Based Optimizer (GBO) is a highly mathematics‐based metaheuristic algorithm that has garnered significant attention since its introduction. It offers several inherent advantages, such as low computational complexity, rapid convergence, and easy implementation. However, GBO some drawbacks, including lack of population diversity tendency to get trapped in local optima. To address these shortcomings, this research introduces an improved version (iGBO). In iGBO, introducing the Sobol sequence strategy ensures higher‐quality initial enhances convergence speed. Additionally, new modified Local Escaping Operator (LEO) proposed, which incorporates sine‐cosine operator DCS/Xbest/Current‐to‐2rand strategy. This LEO improves optimization efficiency boosts search capability, helping avoid The superiority iGBO thoroughly verified through comparisons with original well‐known newly developed algorithms on IEEE CEC’2022 benchmark suite. Furthermore, proposed approach applied extract photovoltaic system’s global maximum power point (MPP) under shading conditions. Three different patterns are considered assess reliability iGBO. performance compared leading algorithms, Particle Swarm Optimization (PSO), Reptile Search Algorithm (RSA), Black Widow (BWOA), Pelican OA (POA), Chimp (ChOA), Osprey (OOA), GBO. results reveal iGBO‐based MPPT consistently outperforms competitors identifying MPP various conditions followed by PSO, while RSA performs least effectively.

Language: Английский

The Differentiated Creative Search (DCS): Leveraging differentiated knowledge-acquisition and creative realism to address complex optimization problems DOI
Poomin Duankhan, Khamron Sunat, Sirapat Chiewchanwattana

et al.

Expert Systems with Applications, Journal Year: 2024, Volume and Issue: 252, P. 123734 - 123734

Published: March 22, 2024

Language: Английский

Citations

23
Intrusion detection in metaverse environment internet of things systems by metaheuristics tuned two level framework DOI Creative Commons
Miloš Antonijević, Miodrag Živković, Milica Djurić-Jovičić

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Jan. 28, 2025

Internet of Things (IoT) is one the most important emerging technologies that supports Metaverse integrating process, by enabling smooth data transfer among physical and virtual domains. Integrating sensor devices, wearables, smart gadgets into environment enables IoT to deepen interactions enhance immersion, both crucial for a completely integrated, data-driven Metaverse. Nevertheless, because devices are often built with minimal hardware connected Internet, they highly susceptible different types cyberattacks, presenting significant security problem maintaining secure infrastructure. Conventional techniques have difficulty countering these evolving threats, highlighting need adaptive solutions powered artificial intelligence (AI). This work seeks improve trust in edge integrated study revolves around hybrid framework combines convolutional neural networks (CNN) machine learning (ML) classifying models, like categorical boosting (CatBoost) light gradient-boosting (LightGBM), further optimized through metaheuristics optimizers leveraged performance. A two-leveled architecture was designed manage intricate data, detection classification attacks within networks. thorough analysis utilizing real-world network dataset validates proposed architecture's efficacy identification specific variants malevolent assaults, classic multi-class challenge. Three experiments were executed open public, where top models attained supreme accuracy 99.83% classification. Additionally, explainable AI methods offered valuable supplementary insights model's decision-making supporting future collection efforts enhancing systems.

Language: Английский

Citations

2
Metaheuristic Optimization Algorithms: an overview DOI Creative Commons
Brahim Benaissa, Masakazu Kobayashi, Musaddiq Al Ali

et al.

Deleted Journal, Journal Year: 2024, Volume and Issue: unknown

Published: Feb. 5, 2024

Metaheuristic optimization algorithms are known for their versatility and adaptability, making them effective tools solving a wide range of complex problems. They don't rely on specific problem types, gradients, can explore globally while handling multi-objective optimization. strike balance between exploration exploitation, contributing to advancements in However, it's important note limitations, including the lack guaranteed global optimum, varying convergence rates, somewhat opaque functioning. In contrast, metaphor-based algorithms, intuitively appealing, have faced controversy due potential oversimplification unrealistic expectations. Despite these considerations, metaheuristic continue be widely used tackling This research paper aims fundamental components concepts that underlie focusing use search references delicate exploitation. Visual representations behavior selected will also provided.

Language: Английский

Citations

15
Efficient economic operation based on load dispatch of power systems using a leader white shark optimization algorithm DOI Creative Commons
Mohamed H. Hassan, Salah Kamel, Ali Selim

et al.

Neural Computing and Applications, Journal Year: 2024, Volume and Issue: 36(18), P. 10613 - 10635

Published: March 27, 2024

Abstract This article proposes the use of a leader white shark optimizer (LWSO) with aim improving exploitation conventional (WSO) and solving economic operation-based load dispatch (ELD) problem. The ELD problem is crucial aspect power system operation, involving allocation generation resources to meet demand while minimizing operational costs. proposed approach aims enhance performance efficiency WSO by introducing leadership mechanism within optimization process, which aids in more effectively navigating complex solution space. LWSO achieves increased utilizing leader-based mutation selection throughout each sharks. efficacy algorithm tested on 13 engineer benchmarks non-convex problems from CEC 2020 compared recent metaheuristic algorithms such as dung beetle (DBO), WSO, fox (FOX), moth-flame (MFO) algorithms. also used address different case studies (6 units, 10 11 40 units), 20 separate runs using other competitive being statistically assessed demonstrate its effectiveness. results show that outperforms algorithms, achieving best for minimum fuel cost Additionally, statistical tests are conducted validate competitiveness algorithm.

Language: Английский

Citations

14
Bald eagle search algorithm: a comprehensive review with its variants and applications DOI Creative Commons
M.A. El‐Shorbagy, Anas Bouaouda, Hossam A. Nabwey

et al.

Systems Science & Control Engineering, Journal Year: 2024, Volume and Issue: 12(1)

Published: Aug. 1, 2024

Bald Eagle Search (BES) is a recent and highly successful swarm-based metaheuristic algorithm inspired by the hunting strategy of bald eagles in capturing prey. With its remarkable ability to balance global local searches during optimization, BES effectively addresses various optimization challenges across diverse domains, yielding nearly optimal results. This paper offers comprehensive review research on BES. Beginning with an introduction BES's natural inspiration conceptual framework, it explores modifications, hybridizations, applications domains. Then, critical evaluation performance provided, offering update effectiveness compared recently published algorithms. Furthermore, presents meta-analysis developments outlines potential future directions. As swarm-inspired algorithms become increasingly important tackling complex problems, this study valuable resource for researchers aiming understand algorithms, mainly focusing comprehensively. It investigates evolution, exploring solving intricate fields.

Language: Английский

Citations

11
Recent applications and advances of African Vultures Optimization Algorithm DOI Creative Commons
Abdelazim G. Hussien, Farhad Soleimanian Gharehchopogh, Anas Bouaouda

et al.

Artificial Intelligence Review, Journal Year: 2024, Volume and Issue: 57(12)

Published: Oct. 17, 2024

Language: Английский

Citations

9
Hybrid LSTM-PSO optimization techniques for enhancing wind power bidding efficiency in electricity markets DOI Creative Commons
Anh Viet Truong, Ngoc Sang Dinh, Thanh Long Duong

et al.

Ain Shams Engineering Journal, Journal Year: 2025, Volume and Issue: 16(2), P. 103285 - 103285

Published: Feb. 1, 2025

Language: Английский

Citations

1
Chinese Pangolin Optimizer: a novel bio-inspired metaheuristic for solving optimization problems DOI
Zhiqing Guo, Guangwei Liu, Feng Jiang

et al.

The Journal of Supercomputing, Journal Year: 2025, Volume and Issue: 81(4)

Published: Feb. 17, 2025

Language: Английский

Citations

1
Optimal Nodes Localization in Wireless Sensor Networks Using Nutcracker Optimizer Algorithms: Istanbul Area DOI Creative Commons
Nabil Neggaz, Amir Seyyedabbasi, Abdelazim G. Hussien

et al.

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 67986 - 68002

Published: Jan. 1, 2024

Node localization is a non-deterministic polynomial time (NP-hard) problem in Wireless Sensor Networks (WSN). It involves determining the geographical position of each node network. For many applications WSNs, such as environmental monitoring, security health and agriculture, precise location nodes crucial. As result this study, we propose novel efficient way to solve without any regard environment, well predetermined conditions. This proposed method based on new Nutcracker Optimization Algorithm (NOA). By utilizing algorithm, it possible maximize coverage rates, decrease numbers, maintain connectivity. Several algorithms were used Grey Wolf (GWO), Kepler Algorithms (KOA), Harris Hawks Optimizer (HHO), Radient-Based (GBO) Gazelle (GOA). The was first tested Istanbul, Turkey, where determined be suitable study area. metaheuristic-based approach distributed architecture, scalable large-scale networks. Among these metaheuristic algorithms, NOA, KOA, GWO have achieved significant performance terms rates (CR), achieving 96.15%, 87.76%, 93.49%, respectively. In their ability sensor problems, proven effective.

Language: Английский

Citations

7
Large-Scale Benchmarking of Metaphor-Based Optimization Heuristics DOI
Diederick Vermetten, Carola Doerr, Hao Wang

et al.

Proceedings of the Genetic and Evolutionary Computation Conference, Journal Year: 2024, Volume and Issue: unknown, P. 41 - 49

Published: July 8, 2024

The number of proposed iterative optimization heuristics is growing steadily, and with this growth, there have been many points discussion within the wider community.One particular criticism that raised towards new algorithms their focus on metaphors used to present method, rather than emphasizing potential algorithmic contributions.Several studies into popular metaphor-based highlighted these problems, even showcasing are functionally equivalent older existing methods.Unfortunately, detailed approach not scalable whole set algorithms.Because this, we investigate ways in which benchmarking can shed light algorithms.To end, run a 294 algorithm implementations BBOB function suite.We how choice budget, performance measure, or other aspects experimental design impact comparison algorithms.Our results emphasize why key step expanding our understanding space, what challenges still need be overcome fully gauge improvements state-of-the-art hiding behind metaphors. CCS CONCEPTS• Theory computation → Design analysis algorithms; Bio-inspired optimization.

Language: Английский

Citations

6