Artificial Optimizer Algorithm for Power System Stabilizer design problem and multidisciplinary engineering applications DOI Creative Commons
Narinder Singh, Mandeep Kaur, Essam H. Houssein

и другие.

Heliyon, Год журнала: 2024, Номер 10(22), С. e40068 - e40068

Опубликована: Ноя. 1, 2024

The novelty of this research lies in presenting a fresh stochastic algorithm enthused via 'Velociraptor' social intelligence wildlife (or nature), known as the Velociraptor Group Optimization (VROA). In strategy, co-operative natural life cycle is mathematically framed, and novel mechanisms are presented to perform search exploration) hunting exploitation). This suggests that proposed method reveals noteworthy ability both exploitation exploration. Furthermore, it successfully stabilities exploration exploitation, supporting process. direction evaluating VROA, we utilized on 51 CEC'17, CEC'20, CEC'22 standard benchmark suites six multidisciplinary engineering optimization functions. well-known statistical methods like Wilcoxon rank-sum test Friedman's have been used verify strength against various optimizers. tabulated numerical solutions show VROA performs better than recent optimizers most has efficiently attained competent resolution while concurrently upholding adherence designated constraints. results validate can offer efficient accurate optimal evaluation with metaheuristics.

Язык: Английский

MORIME: A Multi-Objective RIME Optimization Framework for Efficient Truss Design DOI Creative Commons
Mohammad Aljaidi, Nikunj Mashru, Pinank Patel

и другие.

Results in Engineering, Год журнала: 2025, Номер 25, С. 103933 - 103933

Опубликована: Янв. 5, 2025

Язык: Английский

Процитировано

8
A new enhanced grey wolf optimizer to improve geospatially subsurface analyses DOI

Reza Iraninezhad,

Reza Asheghi, Hassan Ahmadi

и другие.

Modeling Earth Systems and Environment, Год журнала: 2025, Номер 11(2)

Опубликована: Янв. 31, 2025

Язык: Английский

Процитировано

1
Reshaping Industrial Maintenance with Machine Learning: Fouling Control Using Optimized Gaussian Process Regression DOI Creative Commons
Francesco Negri, Andrea Galeazzi, Francesco Gallo

и другие.

Industrial & Engineering Chemistry Research, Год журнала: 2025, Номер unknown

Опубликована: Март 14, 2025

Язык: Английский

Процитировано

0
Optimizing N-1 Contingency Rankings Using a Nature-Inspired Modified Sine Cosine Algorithm DOI Creative Commons

Irnanda Priyadi,

Novalio Daratha, Teddy Surya Gunawan

и другие.

IIUM Engineering Journal, Год журнала: 2025, Номер 26(1), С. 398 - 419

Опубликована: Янв. 10, 2025

Ensuring the reliability and sustainability of power systems is essential for maintaining efficient uninterrupted operations, especially under varying load conditions potential faults. This study tackles critical task contingency ranking by evaluating severity disturbances caused transmission line disconnections. Such evaluations enable system operators to make informed strategic decisions during real-time scenarios. A novel approach utilizing Modified Sine Cosine Algorithm (MSCA), a nature-inspired metaheuristic optimization technique, proposed resolve (N-1) rankings efficiently. The MSCA method validated using IEEE 30-bus test case, focusing on optimal parameter tuning population size, iterations, key variables. Results demonstrate that achieves high capture ratio 96.67%, explores only 8.33 × 10??% search space, requires processing time 3.69 seconds. Compared with established methods such as Ant Colony Optimization (ACO) Genetic (GA), exhibits superior computational efficiency while competitive accuracy. These findings underline in applications where speed precision are critical. By closely matching manual rankings, integrates assessment techniques, offering practical value improving resilience reducing risks associated disruptions. research advances state-of-the-art approaches, providing planners robust tool addressing complex challenges. ABSTRAK: Memastikan keandalan dan kelestarian sistem tenaga elektrik adalah penting untuk mengekalkan operasi yang cekap tidak terganggu, terutamanya dalam menghadapi keadaan beban berubah-ubah kemungkinan kerosakan. Kajian ini menangani tugas kritikal perangkingan kontingensi dengan menilai tahap keparahan gangguan disebabkan oleh pemutusan talian penghantaran. Penilaian sebegini membolehkan pengendali membuat keputusan berinformasi strategik senario masa nyata. Pendekatan baharu menggunakan satu teknik pengoptimuman metaheuristik diilhamkan alam, dicadangkan menyelesaikan cekap. Kaedah disahkan kes ujian memberi tumpuan kepada penalaan optimum saiz populasi, iterasi, pemboleh ubah utama. Keputusan menunjukkan bahawa mencapai nisbah tangkapan tinggi sebanyak hanya meneroka daripada ruang pencarian, memerlukan pemprosesan saat. Berbanding kaedah sedia ada seperti kecekapan pengiraan unggul sambil ketepatan kompetitif. Penemuan menekankan potensi aplikasi nyata di mana kelajuan kritikal. Dengan hasil hampir menyamai manual, mengintegrasikan penilaian pengoptimuman, memberikan nilai praktikal meningkatkan daya tahan mengurangkan risiko berkaitan gangguan. Penyelidikan memajukan pendekatan terkini menyediakan perancang alat kukuh cabaran kompleks.

Процитировано

0
Advanced computational techniques: Bridging metaheuristic optimization and deep learning for material design through image enhancement DOI
Jagrati Talreja,

Divya Chauhan

Elsevier eBooks, Год журнала: 2025, Номер unknown, С. 197 - 228

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0
Gaussian combined arms algorithm: a novel meta-heuristic approach for solving engineering problems DOI

Reza Etesami,

Mohsen Madadi, Farshid Keynia

и другие.

Evolutionary Intelligence, Год журнала: 2025, Номер 18(2)

Опубликована: Март 18, 2025

Язык: Английский

Процитировано

0
Cloud Drift Optimization (CDO) Algorithm: A Nature-Inspired Metaheuristic DOI
Mohammad Alibabaei Shahraki

Research Square (Research Square), Год журнала: 2025, Номер unknown

Опубликована: Апрель 14, 2025

Abstract This study introduces the Cloud Drift Optimization (CDO) algorithm, an innovative nature-inspired metaheuristic approach to solving complex optimization problems. The CDO algorithm mimics dynamic behavior of cloud particles influenced by atmospheric forces, striking a refined balance between exploration and exploitation. It features adaptive weight adjustment mechanism that alters cloud's drift in real-time, allowing for efficient navigation through search space. Using cloud-based strategy, harnesses probabilistic movements maneuver landscape more effectively. has undergone rigorous testing against various established unimodal multimodal benchmark functions, where it showcases outstanding performance characterized faster convergence rates, high robustness, exceptional solution accuracy compared top contemporary techniques. Additionally, applies numerous real-world engineering tasks, such as designing cantilever beams, three-bar trusses, tension/compression springs, pressure vessels. empirical data highlight CDO's ability deliver solutions across fields, machine learning applications, other practical scenarios. These results indicate is promising tool tackling highly multidimensional problems academic industrial environments.

Язык: Английский

Процитировано

0
Optimal distributed generation placement and sizing using modified grey wolf optimization and ETAP for power system performance enhancement and protection adaptation DOI Creative Commons

Nasreddine Bouchikhi,

Fethi Boussadia, Riyadh Bouddou

и другие.

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Апрель 22, 2025

Язык: Английский

Процитировано

0
An enhanced Walrus Optimizer with opposition-based learning and mutation strategy for data clustering DOI Creative Commons
Laith Abualigah, Saleh Ali Alomari,

Mohammad H. Almomani

и другие.

Array, Год журнала: 2025, Номер unknown, С. 100409 - 100409

Опубликована: Май 1, 2025

Язык: Английский

Процитировано

0
Artificial Optimizer Algorithm for Power System Stabilizer design problem and multidisciplinary engineering applications DOI Creative Commons
Narinder Singh, Mandeep Kaur, Essam H. Houssein

и другие.

Heliyon, Год журнала: 2024, Номер 10(22), С. e40068 - e40068

Опубликована: Ноя. 1, 2024

The novelty of this research lies in presenting a fresh stochastic algorithm enthused via 'Velociraptor' social intelligence wildlife (or nature), known as the Velociraptor Group Optimization (VROA). In strategy, co-operative natural life cycle is mathematically framed, and novel mechanisms are presented to perform search exploration) hunting exploitation). This suggests that proposed method reveals noteworthy ability both exploitation exploration. Furthermore, it successfully stabilities exploration exploitation, supporting process. direction evaluating VROA, we utilized on 51 CEC'17, CEC'20, CEC'22 standard benchmark suites six multidisciplinary engineering optimization functions. well-known statistical methods like Wilcoxon rank-sum test Friedman's have been used verify strength against various optimizers. tabulated numerical solutions show VROA performs better than recent optimizers most has efficiently attained competent resolution while concurrently upholding adherence designated constraints. results validate can offer efficient accurate optimal evaluation with metaheuristics.

Язык: Английский

Процитировано

0