Algorithms for intelligent systems, Journal Year: 2025, Volume and Issue: unknown, P. 23 - 45
Published: Jan. 1, 2025
Language: Английский
Algorithms for intelligent systems, Journal Year: 2025, Volume and Issue: unknown, P. 23 - 45
Published: Jan. 1, 2025
Language: Английский
Expert Systems with Applications, Journal Year: 2023, Volume and Issue: 227, P. 120367 - 120367
Published: May 6, 2023
Language: Английский
Citations
37Neural Processing Letters, Journal Year: 2023, Volume and Issue: 55(5), P. 6443 - 6531
Published: Feb. 14, 2023
Language: Английский
Citations
26Applied Sciences, Journal Year: 2022, Volume and Issue: 12(22), P. 11514 - 11514
Published: Nov. 13, 2022
In view of the slow convergence speed traditional particle swarm optimization algorithms, which makes it easy to fall into local optimum, this paper proposes an OTSU multi-threshold image segmentation based on improved algorithm. After completes iterative update and position, method calculating contribution degree is used obtain approximate position direction, reduces scope search. At same time, asynchronous monotone increasing social learning factor decreasing individual are balance global Finally, chaos introduced increase diversity population achieve (IPSO). Twelve benchmark functions selected test performance algorithm compared with meta-heuristic The results show robustness superiority standard dataset images for experiments, some algorithms compare calculation efficiency, peak signal noise ratio (PSNR), structural similarity (SSIM), feature (FSIM), fitness value (FITNESS). that running time 30% faster than other in general, accuracy also better algorithms. Experiments proposed can higher efficiency.
Language: Английский
Citations
32Biomedical Signal Processing and Control, Journal Year: 2022, Volume and Issue: 80, P. 104246 - 104246
Published: Oct. 20, 2022
Language: Английский
Citations
31Archives of Computational Methods in Engineering, Journal Year: 2023, Volume and Issue: 30(6), P. 3791 - 3844
Published: April 12, 2023
Language: Английский
Citations
20Journal of Artificial Intelligence and Soft Computing Research, Journal Year: 2024, Volume and Issue: 14(3), P. 207 - 235
Published: June 1, 2024
Abstract Equilibrium optimizer (EO) is a novel metaheuristic algorithm that exhibits superior performance in solving global optimization problems, but it may encounter drawbacks such as imbalance between exploration and exploitation capabilities, tendency to fall into local tricky multimodal problems. In order address these this study proposes ensemble called hybrid moth equilibrium (HMEO), leveraging both the flame (MFO) EO. The proposed approach first integrates potential of EO then introduces capability MFO help enhance search, fine-tuning, an appropriate balance during search process. To verify algorithm, suggested HMEO applied on 29 test functions CEC 2017 benchmark suite. results developed method are compared with several well-known metaheuristics, including basic EO, MFO, some popular variants. Friedman rank employed measure newly statistically. Moreover, introduced has been mobile robot path planning (MRPP) problem investigate its problem-solving ability real-world experimental show reported comparative approaches.
Language: Английский
Citations
8Archives of Computational Methods in Engineering, Journal Year: 2023, Volume and Issue: 31(3), P. 1659 - 1700
Published: Nov. 30, 2023
Language: Английский
Citations
13Mathematics and Computers in Simulation, Journal Year: 2022, Volume and Issue: 202, P. 295 - 315
Published: June 3, 2022
Language: Английский
Citations
19PLoS ONE, Journal Year: 2023, Volume and Issue: 18(5), P. e0285211 - e0285211
Published: May 5, 2023
Aerial photography is a long-range, non-contact method of target detection technology that enables qualitative or quantitative analysis the target. However, aerial images generally have certain chromatic aberration and color distortion. Therefore, effective segmentation can further enhance feature information reduce computational difficulty for subsequent image processing. In this paper, we propose an improved version Golden Jackal Optimization, which dubbed Helper Mechanism Based Optimization (HGJO), to apply multilevel threshold images. The proposed uses opposition-based learning boost population diversity. And new approach calculate prey escape energy improve convergence speed algorithm. addition, Cauchy distribution introduced adjust original update scheme exploration capability Finally, novel “helper mechanism” designed performance local optima. To demonstrate effectiveness algorithm, use CEC2022 benchmark function test suite perform comparison experiments. HGJO compared with GJO five classical meta-heuristics. experimental results show able achieve competitive in set. all algorithms are applied experiments variable images, segmented by beat others. Noteworthy, source code publicly available at https://github.com/Vang-z/HGJO .
Language: Английский
Citations
12Journal of Bionic Engineering, Journal Year: 2024, Volume and Issue: 21(3), P. 1465 - 1495
Published: April 26, 2024
Language: Английский
Citations
4