Training Feedforward Neural Networks Using Arithmetic Optimization Algorithm for Medical Classification DOI
Koon Meng Ang, Wei Hong Lim, Sew Sun Tiang

et al.

Lecture notes in electrical engineering, Journal Year: 2023, Volume and Issue: unknown, P. 313 - 323

Published: Jan. 1, 2023

Language: Английский

A Modified Particle Swarm Optimization for Efficient Maximum Power Point Tracking Under Partial Shading Condition DOI
Jia Shun Koh, Rodney H.G. Tan, Wei Hong Lim

et al.

IEEE Transactions on Sustainable Energy, Journal Year: 2023, Volume and Issue: 14(3), P. 1822 - 1834

Published: March 1, 2023

Particle swarm optimization (PSO) is envisioned as potential solution to overcome maximum power point tracking (MPPT) problems. Nevertheless, conventional PSO suffers from large transient oscillation, slow convergence and tedious parameter tuning when global MPP (GMPP) under partial shading conditions (PSC), leading poor efficiency significant loss. Therefore, a modified hybridized with adaptive local search (MPSO-HALS) designed robust, real-time MPPT algorithm. A initialization scheme that leverages grid partitioning oppositional-based learning incorporated produce an evenly distributed initial population across P-V curve. Additionally, rank-based selection adopted choose best half of for subsequent modes. method fewer parameters devised rapidly identify approximated location GMPP. Finally, using Perturb Observe step size (P&O-ASM) proposed refine the near-optimal duty cycle track GMPP negligible oscillations. MPSO-HALS implemented into low-cost microcontroller application. Extensive studies prove algorithm outperforms bat (BA), improved grey wolf optimizer (IGWO), P&O, time shorter than 0.3 s accuracy above 99% different complex PSCs.

Language: Английский

Citations

45
Research on time-series based and similarity search based methods for PV power prediction DOI
Meng Jiang, Kun Ding, Xiang Chen

et al.

Energy Conversion and Management, Journal Year: 2024, Volume and Issue: 308, P. 118391 - 118391

Published: April 9, 2024

Language: Английский

Citations

10
Dynamic opposition learning-based rank-driven teaching learning optimizer for parameter extraction of photovoltaic models DOI Creative Commons
Xu‐Ming Wang, Wen Zhang

Alexandria Engineering Journal, Journal Year: 2025, Volume and Issue: 117, P. 325 - 339

Published: Jan. 16, 2025

Language: Английский

Citations

1
An evolutionary stacked generalization model based on deep learning and improved grasshopper optimization algorithm for predicting the remaining useful life of PEMFC DOI
Chu Zhang,

Haowen Hu,

Jie Ji

et al.

Applied Energy, Journal Year: 2022, Volume and Issue: 330, P. 120333 - 120333

Published: Nov. 23, 2022

Language: Английский

Citations

35
Evolved opposition-based Mountain Gazelle Optimizer to solve optimization problems DOI Creative Commons

Priteesha Sarangi,

Prabhujit Mohapatra

Journal of King Saud University - Computer and Information Sciences, Journal Year: 2023, Volume and Issue: 35(10), P. 101812 - 101812

Published: Oct. 26, 2023

A recently established swarm-based algorithm, namely, Mountain Gazelle Optimizer (MGO) which draws inspiration from social structure and hierarchy of wild mountain gazelles is competitive for solving optimization problems. However, the MGO has some drawbacks: when dealing with higher dimensions, early iterations could become stuck in suboptimal search area. It would be difficult to abandon local optimal solution if best solutions neglect relevant space. Therefore, overcome these limitations, this paper offers an Evolved Opposition-based Learning (EOBL) mechanism helps algorithm jump out optima while accelerating convergence speed. This novel incorporating propose (EOBMGO). The experiments are conducted CEC2005 CEC2019 benchmark functions, along seven engineering challenges examine performance proposed EOBMGO. Furthermore, statistical tests, like t-test Wilcoxon rank-sum test, verified demonstrate that EOBMGO outperforms existing top-performing algorithms. outcomes indicated technique may seen as efficient successful approach complex challenges.

Language: Английский

Citations

17
Methods to balance the exploration and exploitation in Differential Evolution from different scales: A survey DOI
Yanyun Zhang, Guanyu Chen, Cheng Li

et al.

Neurocomputing, Journal Year: 2023, Volume and Issue: 561, P. 126899 - 126899

Published: Oct. 7, 2023

Language: Английский

Citations

14
Differential Evolution: A Survey on Their Operators and Variants DOI

Elivier Reyes-Dávila,

Eduardo H. Haro, Ángel Casas-Ordaz

et al.

Archives of Computational Methods in Engineering, Journal Year: 2024, Volume and Issue: unknown

Published: May 23, 2024

Language: Английский

Citations

5
CGH-GTO method for model parameter identification based on improved grey wolf optimizer, honey badger algorithm, and gorilla troops optimizer DOI
Meng Jiang, Kun Ding, Xiang Chen

et al.

Energy, Journal Year: 2024, Volume and Issue: 296, P. 131163 - 131163

Published: April 1, 2024

Language: Английский

Citations

4
An advanced initialization technique for metaheuristic optimization: a fusion of Latin hypercube sampling and evolutionary behaviors DOI
Héctor Escobar, Erik Cuevas, Karla Avila

et al.

Computational and Applied Mathematics, Journal Year: 2024, Volume and Issue: 43(4)

Published: May 9, 2024

Language: Английский

Citations

4
Security-Aware Optimal Cluster Head-Based Energy-Efficient Data Transmission on Wireless Sensor Network DOI

A. D. Bharath,

N. Revathy

Smart innovation, systems and technologies, Journal Year: 2025, Volume and Issue: unknown, P. 261 - 277

Published: Jan. 1, 2025

Language: Английский

Citations

0