A Radial Visualization Method based on Knee Point Information for Many-objective Optimization DOI
Li Gui, Wenguang Lin

Applied Soft Computing, Journal Year: 2025, Volume and Issue: unknown, P. 113064 - 113064

Published: April 1, 2025

Language: Английский

Global polynomial synchronization for quaternion-valued T–S fuzzy inertial neural networks via event-triggered control: A polynomial gain method DOI
Jingjing Zhang, Zhouhong Li, Jinde Cao

et al.

Chaos Solitons & Fractals, Journal Year: 2025, Volume and Issue: 196, P. 116403 - 116403

Published: April 15, 2025

Language: Английский

Citations

1
A novel chemical property-based, alignment-free scalable feature extraction method for genomic data clustering DOI
Rajesh Dwivedi, Aruna Tiwari, Neha Bharill

et al.

Computers & Electrical Engineering, Journal Year: 2025, Volume and Issue: 123, P. 110175 - 110175

Published: Feb. 18, 2025

Language: Английский

Citations

0
Chaotic dynamics of discrete memristor-coupled Sinh map DOI
Mohammad Saeed Feali

Chaos Solitons & Fractals, Journal Year: 2025, Volume and Issue: 196, P. 116480 - 116480

Published: April 19, 2025

Language: Английский

Citations

0
Basketball motion recognition and tracking method based on improved convolutional neural network DOI Creative Commons
Yan Gong

Systems and Soft Computing, Journal Year: 2025, Volume and Issue: 7, P. 200272 - 200272

Published: May 1, 2025

Language: Английский

Citations

0
MHO: A Modified Hippopotamus Optimization Algorithm for Global Optimization and Engineering Design Problems DOI Creative Commons
Tao Han, Haiyan Wang, Tingting Li

et al.

Biomimetics, Journal Year: 2025, Volume and Issue: 10(2), P. 90 - 90

Published: Feb. 5, 2025

The hippopotamus optimization algorithm (HO) is a novel metaheuristic that solves problems by simulating the behavior of hippopotamuses. However, traditional HO may encounter performance degradation and fall into local optima when dealing with complex global engineering design problems. In order to solve these problems, this paper proposes modified (MHO) enhance convergence speed solution accuracy introducing sine chaotic map initialize population, changing factor in growth mechanism, incorporating small-hole imaging reverse learning strategy. MHO tested on 23 benchmark functions successfully three According experimental data, obtains optimal 13 exits optimum faster, has better ordering stability than other nine metaheuristics. This study algorithm, which offers fresh insights practical parameter optimization.

Language: Английский

Citations

0
A Radial Visualization Method based on Knee Point Information for Many-objective Optimization DOI
Li Gui, Wenguang Lin

Applied Soft Computing, Journal Year: 2025, Volume and Issue: unknown, P. 113064 - 113064

Published: April 1, 2025

Language: Английский

Citations

0