A comparative analysis of global optimization algorithms for surface electromyographic signal onset detection

Alam, Shafiq; Xin, Zhao; Niazi, Imran Khan; Ayub, Muhammad Sohaib; Khan, Muhammad Asad

doi:10.1016/j.dajour.2023.100294

Cited by 4 publications

(2 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It has also excelled in the optimization of Brayton cycles with solar technologies and dual regenerative systems, effectively achieving irreversibility minimization [36]. In other areas, such as sEMG signal detection and the identification of optimal parameter sets for solar water heaters, PSO has demonstrated precision and effectiveness [37,38]. Additionally, [57] proposes the use of Enhanced Particle Swarm Optimization (EPSO) to minimize energy losses in electrical networks, overcoming the limitations of conventional PSO, while [58] introduces a novel variant, PSO_ML-FSSO, for the Maximum Power Point Tracking (MPPT) task in photovoltaic solar systems, surpassing other known methods in efficiency and settling time.…”

Section: Optimization Techniquementioning

confidence: 99%

“…In various studies, Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) have proven their effectiveness [30][31][32][33][34][35][36][37][38][39][40]. However, PSO stands out in engineering and sciences due to its adaptability, simple structure, fast convergence, ease of implementation, and having fewer parameters, positioning it as a versatile algorithm with superior performance compared to other heuristic algorithms [31,32,34,35].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optimization of Ampacity in High-Voltage Underground Cables with Thermal Backfill Using Dynamic PSO and Adaptive Strategies

Atoccsa,

Puma,

Mendoza

et al. 2024

Energies

View full text Add to dashboard Cite

This article addresses challenges in the design of underground high-voltage transmission lines, focusing on thermal management and cable ampacity determination. It introduces an innovative proposal that adjusts the dimensions of the backfill to enhance ampacity, contrasting with the conventional approach of increasing the core cable’s cross-sectional area. The methodology employs a particle swarm optimization (PSO) technique with adaptive penalization and restart strategies, implemented in MATLAB for parameter autoadaptation. The article emphasizes more efficient solutions than traditional PSO, showcasing improved convergence and precise results (success probability of 66.1%). While traditional PSO is 81% faster, the proposed PSO stands out for its accuracy. The inclusion of thermal backfill results in an 18.45% increase in cable ampacity, considering variations in soil thermal resistivity, backfill properties, and ambient temperature. Additionally, a sensitivity analysis was conducted, revealing conservative values that support the proposal’s robustness. This approach emerges as a crucial tool for underground installation, contributing to continuous ampacity improvement and highlighting its impact on decision making in energy systems.

show abstract