- 标题
- 摘要
- 关键词
- 实验方案
- 产品
-
Performance comparison of recent optimization algorithm Jaya with particle swarm optimization for digital image watermarking in complex wavelet domain
摘要： Nowadays copyright protection is mandatory in the field of image processing to removes the illegitimate utilization and imitation of digital images. The digital image watermarking is one of the most reliable methods for protecting the illegal validation of data. In this paper, singular value decomposition based digital image watermarking scheme is proposed in complex wavelet transform (CWT) domain using intelligence algorithms like particle swarm optimization (PSO) and recently proposed Jaya algorithm. The watermark image is embedded into high frequency CWT subband of cover image. At the time of watermark embedding and extraction, optimization algorithms Jaya and PSO are applied to improve the robustness and imperceptibility by assessing the fitness function. The perceptual quality of watermarked image and robustness of extracted watermark image are verified under the filtering, rotation, scaling, Gaussian noise and JPEG compression attacks. From the comparative analysis it is proved that Jaya algorithm is better as compared to PSO algorithm under most types of attacks with higher magnitudes whereas identical under the lower magnitude of applied attacks. Moreover, using variety of cover images, it is found that, the elapse time and value of fitness function given by Jaya algorithm are also better as compared to PSO.
关键词： Particle swarm optimization,Singular value decomposition,Jaya algorithm,Complex wavelet domain watermarking,Fitness function,Elapse time
更新于2025-09-23 15:21:01
-
A performance-guided JAYA algorithm for parameters identification of photovoltaic cell and module
摘要： In order to carry out the evaluation, control and maximum power point tracking on photovoltaic (PV) systems, accurate and reliable model parameter identification of PV cell and module is always desired. In this study, a performance-guided JAYA (PGJAYA) algorithm is proposed for extracting parameters of different PV models. In proposed PGJAYA algorithm, the individual performance in the whole population is quantified through probability. Then, based on probability, each individual can self-adaptively select different evolution strategies designed for balancing exploration and exploitation abilities to conduct the searching process. Meanwhile, the quantified performance is employed to select the exemplar to construct the promising searching direction. In addition, a self-adaptive chaotic perturbation mechanism is introduced around the current best solution to explore more better solution for replacing the worst one, thus improving the quality of whole population. The parameters estimation performance of PGJAYA is evaluated through three widely used standard datasets of different PV models including single diode, double diode, and PV module. Comparative and statistical results demonstrate that PGJAYA has a superior performance as it always obtains the most accurate parameters with strong robustness among all compared algorithms. Furthermore, the tests based on experimental data from the data sheet of different types of PV modules suggest that the proposed algorithm can achieve superior results at different irradiance and temperature. Based on these superiorities, it is concluded that PGJAYA is a promising parameter identification method for PV cell and module model.
关键词： JAYA algorithm,Optimization,Photovoltaic cell and module,Parameters identification
更新于2025-09-11 14:15:04