Microsystem Technologies, Journal Year: 2024, Volume and Issue: unknown
Published: Sept. 11, 2024
Language: Английский
Circuits Systems and Signal Processing, Journal Year: 2025, Volume and Issue: unknown
Published: Jan. 25, 2025
Language: Английский
Citations
2
IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 134948 - 134984
Published: Jan. 1, 2024
Language: Английский
Citations
4
Optimal Control Applications and Methods, Journal Year: 2025, Volume and Issue: unknown
Published: Feb. 24, 2025
ABSTRACT This paper introduces the modified dandelion optimizer (mDO), a novel adaptive metaheuristic algorithm designed to address complex engineering optimization challenges, with focus on infinite impulse response (IIR) system identification. The proposed mDO incorporates three key advancements: an enhanced descending phase improve global exploration, exploration‐exploitation that balances search intensity and breadth, self‐adaptive crossover operator refines solutions dynamically. These innovations specifically target challenges associated high‐order IIR modeling, enabling deliver more precise efficient To validate its performance, was rigorously evaluated across diverse testing environments, including CEC2017 CEC2022 benchmark functions, various model identification scenarios, real‐world design problems such as multi‐product batch plant design, multiple disk clutch brake speed reducer design. Comparative analyses reveal consistently outperforms leading algorithms in terms of accuracy, robustness, computational efficiency, particularly complex, high‐dimensional landscapes. Statistical assessments further confirm mDO's superior capability accurately identifying parameters even under noise varying orders. study positions competitive versatile tool for applications, offering significant improvements accuracy adaptability advanced modeling problem‐solving.
Language: Английский
Citations
0
PeerJ Computer Science, Journal Year: 2025, Volume and Issue: 11, P. e2722 - e2722
Published: Feb. 28, 2025
The Atom Search Optimization (ASO) algorithm is a recent advancement in metaheuristic optimization inspired by principles of molecular dynamics. It mathematically models and simulates the natural behavior atoms, with interactions governed forces derived from Lennard-Jones potential constraint based on bond-length potentials. Since its inception 2019, it has been successfully applied to various challenges across diverse fields technology science. Despite notable achievements rapidly growing body literature ASO domain, comprehensive study evaluating success implementations still lacking. To address this gap, article provides thorough review half decade advancements research, synthesizing wide range studies highlight key variants, their foundational principles, significant achievements. examines applications, including single- multi-objective problems, introduces well-structured taxonomy guide future exploration ASO-related research. reviewed reveals that several variants algorithm, modifications, hybridizations, implementations, have developed tackle complex problems. Moreover, effectively domains, such as engineering, healthcare medical Internet Things communication, clustering data mining, environmental modeling, security, engineering emerging most prevalent application area. By addressing common researchers face selecting appropriate algorithms for real-world valuable insights into practical applications offers guidance designing tailored specific
Language: Английский
Citations
0
Knowledge and Information Systems, Journal Year: 2025, Volume and Issue: unknown
Published: April 12, 2025
Language: Английский
Citations
0
Symmetry, Journal Year: 2024, Volume and Issue: 16(10), P. 1255 - 1255
Published: Sept. 24, 2024
An infinite impulse response (IIR) system might comprise a multimodal error surface and accurately discovering the appropriate filter parameters for modeling remains complicated. The swarm intelligence algorithms facilitate IIR filter’s by exploring parameter domains exploiting acceptable sets. This paper presents an enhanced symmetric sand cat optimization with multiple strategies (MSSCSO) to achieve adaptive identification. principal objective is recognize most regulating coefficients minimize mean square (MSE) between unidentified system’s input output. MSSCSO cooperative swarms integrates ranking-based mutation operator, elite opposition-based learning strategy, simplex method capture supplementary advantages, disrupt regional extreme solutions, identify finest potential solutions. not only receives extensive exploration exploitation refrain from precocious convergence foster computational efficiency; it also endures robustness reliability demographic variability elevate estimation precision. experimental results manifest that practicality feasibility of are superior those other methods in terms speed, calculation precision, detection efficiency, coefficients, MSE fitness value.
Language: Английский
Citations
1
Microsystem Technologies, Journal Year: 2024, Volume and Issue: unknown
Published: Sept. 11, 2024
Language: Английский
Citations
1