Sadhana, Journal Year: 2024, Volume and Issue: 49(3)
Published: Sept. 3, 2024
Language: Английский
Alexandria Engineering Journal, Journal Year: 2024, Volume and Issue: 106, P. 474 - 504
Published: Aug. 24, 2024
Language: Английский
Citations
7
Biomimetics, Journal Year: 2024, Volume and Issue: 9(1), P. 20 - 20
Published: Jan. 2, 2024
The growing intricacies in engineering, energy, and geology pose substantial challenges for decision makers, demanding efficient solutions real-world production. water flow optimizer (WFO) is an advanced metaheuristic algorithm proposed 2021, but it still faces the challenge of falling into local optima. In order to adapt WFO more effectively specific domains address optimization problems efficiently, this paper introduces enhanced (CCWFO) designed enhance convergence speed accuracy by integrating a cross-search strategy. Comparative experiments, conducted on CEC2017 benchmarks, illustrate superior global capability CCWFO compared other algorithms. application production three-channel reservoir model explored, with focus comparative analysis against several classical evolutionary experimental findings reveal that achieves higher net present value (NPV) within same limited number evaluations, establishing itself as compelling alternative optimization.
Language: Английский
Citations
5
IEEE Sensors Journal, Journal Year: 2024, Volume and Issue: 24(8), P. 13578 - 13588
Published: March 4, 2024
A robust and high-precision localization algorithm is crucial for the valid running of wireless sensor networks. However, conventional algorithms hardly overcome shortcoming low accuracy. Concerning this problem, a novel coevolutionary noise-suppressing Newton method (CNSNM) proposed, which combines advantages (NSNM) particle swarm optimization (PSO) to enhance its accuracy robustness. Specifically, PSO conducts global search using all beacon nodes, NSNM carries out local searches with different node groupings, respectively. When two get their solutions, utilizes solution correct starting point distance errors. Simultaneously, uses best errors, then next begins until termination conditions are satisfied. Simulation results show that proposed has high performance. Further, since CNSNM does not require be equipped specific measuring components, depend on GPS signals, low-computational burden, it possesses potential in generalized application real-world scenarios.
Language: Английский
Citations
4
Computer Networks, Journal Year: 2025, Volume and Issue: 258, P. 111025 - 111025
Published: Jan. 5, 2025
Language: Английский
Citations
0
Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)
Published: Feb. 15, 2025
The exchange of information in Wireless Sensor Networks (WSNs) across different environments, whether they are above the ground, underground, underwater, or space has advanced significantly over time. Among these advancements, precise localization nodes within network remains a key and vital challenge. In context Underwater (UWSNs), plays pivotal role enabling efficient execution diverse underwater applications such as environmental monitoring, disaster management, military surveillance many more. This review article is focusing on three primary aspects, first section focuses fundamentals UWSNs, providing an depth comprehensive discussion various methods. Where we have highlighted two main categories that anchor based free along with their respective subcategories. second this examines challenges may emerge during implementation process. To enhance clarity structure, been carefully analyzed categorized into groups are, (i) Algorithmic challenges, (ii) Technical (iii) Environmental challenges. third begins by presenting latest advancements UWSNs localization, followed exploration how Machine Learning (ML) Deep (DL) models can contribute enhancing evaluate potential benefits ML DL techniques, assessed performance through simulations, metrics error, velocity estimation Root Mean Square Error (RMSE), energy consumption. also aims to provide actionable insights guideline for future research directions opportunities practitioners field localization. Which will ultimately help reliability advancing techniques promoting seamless integration.
Language: Английский
Citations
0
Nonlinear Engineering, Journal Year: 2025, Volume and Issue: 14(1)
Published: Jan. 1, 2025
Abstract With the quick advancement of technology, tennis ball-picking robots have been maturely applied. However, currently, automatic often low accuracy in positioning. To improve robot positioning, a weighted factor and virtual reference label improved indoor positioning algorithm is proposed, combined with radio frequency identification (RFID) technology. This method applied to constructs model based on RFID Comparing effectiveness proposed algorithm, it was found that precision average were 0.983 94.6%, respectively, which better than comparison algorithms. In addition, an analysis conducted this significantly model. Moreover, experiment model, results showed The above outcomes illustrate study good practical value, conducive improving robot’s image target
Language: Английский
Citations
0
Peer-to-Peer Networking and Applications, Journal Year: 2024, Volume and Issue: 17(2), P. 961 - 984
Published: Feb. 7, 2024
Language: Английский
Citations
2
International Journal of Computational Intelligence Systems, Journal Year: 2024, Volume and Issue: 17(1)
Published: April 4, 2024
Abstract The water flow optimizer (WFO) is the latest swarm intelligence algorithm inspired by shape of flow. Its advantages simplicity, efficiency, and robust performance have motivated us to further enhance it. In this paper, we introduce fractional-order (FO) technology with memory properties into WFO, called (FOWFO). To verify superior practicality FOWFO, conducted comparisons nine state-of-the-art algorithms on benchmark functions from IEEE Congress Evolutionary Computation 2017 (CEC2017) four real-world optimization problems large dimensions. Additionally, tuning adjustments were made for two crucial parameters within framework. Finally, an analysis was performed balance between exploration exploitation FOWFO its complexity.
Language: Английский
Citations
2
Biomimetics, Journal Year: 2023, Volume and Issue: 8(5), P. 393 - 393
Published: Aug. 26, 2023
Existing swarm intelligence (SI) optimization algorithms applied to node localization (NLO) and coverage (NCO) problems have low accuracy. In this study, a novel balanced butterfly optimizer (BBO) is proposed which comprehensively considers that butterflies in nature both smell-sensitive light-sensitive characteristics. These characteristics are used for the global local search strategies of algorithm, respectively. Notably, value individuals' characteristic generally positive, point cannot be ignored. The performance BBO verified by twenty-three benchmark functions compared other state-of-the-art (SOTA) SI algorithms, including particle (PSO), differential evolution (DE), grey wolf (GWO), artificial (ABO), algorithm (BOA), Harris hawk (HHO), aquila (AO). results demonstrate has better with ability strong stability. addition, address NLO NCO wireless sensor networks (WSNs) environmental monitoring, obtaining good results.
Language: Английский
Citations
5
IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 39238 - 39260
Published: Jan. 1, 2024
An important service in the wireless systems for human daily life is information of a mobile user location. Wireless sensor network structure that can be used to determine position. The time-difference-of-arrival (TDoA) technique often considered localization due low cost network. In this work, error covariance matrices are derived predicting performance conventional closed-form constrained total least squares (CTLS) estimator. More importantly, three new Newton's methods proposed computing CTLS solution TDoA localization. addition, theoretical both Newton-based approaches provided closed forms. Numerical simulation conducted compare prediction with corresponding actual estimation error. It illustrated two techniques provide better performance, terms lower bias and root mean square error, less computational time, more reliability, than former algorithms. Furthermore, expressions well coincide random results.
Language: Английский
Citations
1