Designing a genetic watermarking approach for 3D scenes

A new watermarking algorithm based on genetic algorithm (GA) in the transform domain is proposed. Unlike the existing computer-generated integral imaging based watermarking methods, the proposed method utilizes GA searching to the optimized transform domain to serve as a trade-off for watermark embedding. In this paper, 3D scene to be captured by using a virtual pinhole array and be computationally recorded as an elemental image array (EIA), watermarking with GA optimization and computer-generated holography is implemented. In the proposed GA optimization process, we utilize the fitness function to improve the visual quality of watermarked images and the robustness. Simulation results show that the proposed algorithm yields a holographic watermark that is imperceptibility to human eyes and robust to standard watermarking attacks. A comparison of the proposed watermarking method to the existing similar watermarking methods demonstrated that the proposed method generally outperforms completing methods in terms of imperceptibility and robustness.     

A robust SVD-based image watermarking using a multi-objective particle swarm optimization

The major objective in developing a robust digital watermarking algorithm is to obtain the highest possible robustness without losing the visual imperceptibility. To achieve this objective, we proposed in this paper an optimal image watermarking scheme using multi-objective particle swarm optimization (MOPSO) and singular value decomposition (SVD) in wavelet domain. Having decomposed the original image into ten sub-bands, singular value decomposition is applied to a chosen detail sub-band. Then, the singular values of the chosen sub-band are modified by multiple scaling factors (MSF) to embed the singular values of watermark image. Various combinations of multiple scaling factors are possible, and it is difficult to obtain optimal solutions. Thus, in order to achieve the highest possible robustness and imperceptibility, multi-objective optimization of the multiple scaling factors is necessary. This work employs particle swarm optimization to obtain optimum multiple scaling factors. Experimental results of the proposed approach show both the significant improvement in term of imperceptibility and robustness under various attacks.     

Shaping the Wavefront of Incident Light with a Strong Robustness Particle Swarm Optimization Algorithm

We demonstrate a modified particle swarm optimization(PSO) algorithm to effectively shape the incident light with strong robustness and short optimization time. The performance of the modified PSO algorithm and genetic algorithm(GA) is numerically simulated. Then, using a high speed digital micromirror device, we carry out light focusing experiments with the modified PSO algorithm and GA. The experimental results show that the modified PSO algorithm has greater robustness and faster convergence speed than GA. This modified PSO algorithm has great application prospects in optical focusing and imaging inside in vivo biological tissue, which possesses a complicated background.     

Blind and Secured Adaptive Digital Image Watermarking Approach for High Imperceptibility and Robustness

In the past decade, rapid development in digital communication has led to prevalent use of digital images. More importantly, confidentiality issues have also come up recently due to the increase in digital image transmission across the Internet. Therefore, it is necessary to provide high imperceptibility and security to digitally transmitted images. In this paper, a novel blind digital image watermarking scheme is introduced tackling secured transmission of digital images, which provides a higher quality regarding both imperceptibility and robustness parameters. A block based hybrid IWT- SVD transform is implemented for robust transmission of digital images. To ensure high watermark security, the watermark is encrypted using a Pseudo random key which is generated adaptively from cover and watermark images. An encrypted watermark is embedded in randomly selected low entropy blocks to increase the security as well as imperceptibility. Embedding positions within the block are identified adaptively using a Blum–Blum–Shub Pseudo random generator. To ensure higher visual quality, Initial Scaling Factor (ISF) is chosen adaptively from a cover image using image range characteristics. ISF can be optimized using Nature Inspired Optimization (NIO) techniques for higher imperceptibility and robustness. Specifically, the ISF parameter is optimized by using three well-known and novel NIO-based algorithms such as Genetic Algorithms (GA), Artificial Bee Colony (ABC), and Firefly Optimization algorithm. Experiments were conducted for the proposed scheme in terms of imperceptibility, robustness, security, embedding rate, and computational time. Experimental results support higher effectiveness of the proposed scheme. Furthermore, performance comparison has been done with some of the existing state-of-the-art schemes which substantiates the improved performance of the proposed scheme.     

二冷配水优化建模与混合自适应PSO算法求解

针对连铸二冷区生产环境复杂且存在着大量水雾干扰的情况,建立了连铸水量优化模型并提出了一种混合的自适应粒子群算法来求解连铸二冷水优化问题。依据冶金过程中的工艺要求建立了二冷水量优化模型,并在经典的PSO算法基础上提出了适合该问题求解了混合自适应PSO算法。由于连铸过程存在着偏微分方程约束,传统的优化方法容易陷入局部最优解,不能达到很好的动态优化效果。研究了粒子群算法,基于种群的多样性,不断的自适应的更新粒子群算法中参数,将禁忌搜索的方法和传统的粒子群算法结合,增强了算法的局部搜索能力和全局寻找全局最优的能力。将该算法应用到连铸二冷水动态优化中,实验结果表面该算法能够快速有效的求解该优化问题。该方法用于连铸二冷水优化是可行的、有效的。     

粒子群及遗传算法在相对论返波管中的应用

在全三维粒子模拟软件CHIPIC平台上,分别开发了粒子群及基因算法模块.以相对论返波管为例,采用三种不同类型的参数(连续参数、离散参数、混合参数),对粒子群及基因算法进行比较.优化结果表明:粒子群算法的收敛速度更快,在有限的迭代步数内得到的目标结果也更优良,总体表现优于基因算法.     

Improved Quantum Evolutionary Computation Based on Particle SwarmOptimization and Two-Crossovers

A quantum evolutionary computation （QEC） algorithm with particle swarm optimization （PSO） and two-crossovers is proposed to overcome identified limitations. PSO is adopted to update the Q-bit automatically, and two-crossovers are applied to improve the convergence quality in the basic QEC model. This hybrid strategy can effectively employ both the ability to jump out of the local minima and the capacity of searching the global optimum. The performance of the proposed approach is compared with basic QEC on the standard unconstrained scalable benchmark problem that numerous hard combinatorial optimization problems can be formulated. The experimental results show that the proposed method outperforms the basic QEC quite significantly.     

群体智能优化中的虚拟碰撞：雨林算法

启发式优化算法中寻优代理过早收敛易陷入局部最优. 本文对此进行机理分析并发现, 虚拟碰撞作为一种隐性过早收敛现象将直接影响群体智能优化算法的准确性与快速性, 而采样过程的无约束性和样本分布信息的缺失是导致虚拟碰撞的根本原因. 为解决上述问题, 本文提出雨林优化算法. 该算法仿照植物生长模式, 利用规模可变种群代替规模限定种群进行分区分级寻优采样, 并结合均匀与非均匀采样原则来权衡优化算法的探索与挖掘, 可以有效减少虚拟碰撞的发生, 在提高寻优效率的同时, 获取精准性和稳定性较高的全局最优解. 与遗传算法、粒子群算法对标称函数的寻优对比实验表明, 雨林算法在快速性、准确性以及泛化能力等方面均具有优势. 关键词： 优化算法 群体智能 进化计算 计算智能     

A Hybrid Rao-NM Algorithm for Image Template Matching

This paper proposes a hybrid Rao-Nelder–Mead (Rao-NM) algorithm for image template matching is proposed. The developed algorithm incorporates the Rao-1 algorithm and NM algorithm serially. Thus, the powerful global search capability of the Rao-1 algorithm and local search capability of NM algorithm is fully exploited. It can quickly and accurately search for the high-quality optimal solution on the basis of ensuring global convergence. The computing time is highly reduced, while the matching accuracy is significantly improved. Four commonly applied optimization problems and three image datasets are employed to assess the performance of the proposed method. Meanwhile, three commonly used algorithms, including generic Rao-1 algorithm, particle swarm optimization (PSO), genetic algorithm (GA), are considered as benchmarking algorithms. The experiment results demonstrate that the proposed method is effective and efficient in solving image matching problems.     

Particle swarm optimization and its application to the design of diffraction grating filters

Particle swarm optimization (PSO) is an evolutionary, easy-to-implement technique to design optical diffraction gratings. Design of reflection and transmission guided-mode resonance (GMR) grating filters using PSO is reported. The spectra of the designed filters are in good agreement with the design targets in a reasonable computation time. Also, filters are designed with a genetic algorithm (GA) and the results obtained by the GA and PSO are compared.     

一种基于离散粒子群优化算法的高光谱图像端元提取方法

针对混合像元分解过程中,由于数据噪声引起的端元提取不准确问题,引入了群智能算法中的粒子群优化算法,并对粒子群优化算法进行了改进,重新定义了位置和速度的表示方法和更新策略,得到离散粒子群优化(discrete particle swarm optimization,D-PSO),能够在离散空间中进行搜索,解决组合优化问题。同时,通过定义目标函数和可行解空间,将端元提取问题改写成组合优化问题,最终实现利用D-PSO进行端元提取。在给出算法的详细流程之后,文章通过一组模拟数据实验和一组实际数据实验验证了D-PSO算法对于具有较大噪声的数据的适应性和提取端元的可信程度,并分析了不同参数对于算法性能的影响。     

Improving the performance of DCT-based fragile watermarking using intelligent optimization algorithms

The performance of a fragile watermarking method based on discrete cosine transform (DCT) has been improved in this paper by using intelligent optimization algorithms (IOA), namely genetic algorithm, differential evolution algorithm, clonal selection algorithm and particle swarm optimization algorithm. In DCT based fragile watermarking techniques, watermark embedding can usually be achieved by modifying the least significant bits of the transformation coefficients. After the embedding process is completed, transforming the modified coefficients from the frequency domain to the spatial domain produces some rounding errors due to the conversion of real numbers to integers. The rounding errors caused by this transformation process were corrected by the use of intelligent optimization algorithms mentioned above. This paper gives experimental results which show the feasibility of using these optimization algorithms for the fragile watermarking and demonstrate the accuracy of these methods. The performance comparison of the algorithms was also realized.     

Gas-solid phase calculations of binary mixtures using optimization of evolutionary algorithms

A comparison between different modern populations based optimization methods applied to the gas-solid phase calculations is presented. Simulations are carried out in twelve binary mixtures containing supercritical carbon dioxide. Particle swarm optimization (PSO) and genetic algorithm (GA) are used to calculate interaction parameters, solubility, and sublimation pressure on these mixtures using the Peng-Robinson equation of state with the Wong-Sandler mixing rules. Comparing PSO with GA shows that the performance of PSO is better than that of GA and that it is a preferable method to optimize parameters of the gas-solid equilibrium.     

基于粒子群优化算法的自适应图像分割方法

提出了一种基于粒子群优化算法的图像分割新方法。粒子群优化(PSO)算法是一类随机全局优化技术,它通过粒子间的相互作用发现复杂搜索空间中的最优区域缩短了寻找阈值的时间。将PSO用于基于改进的最佳加权熵阈值法的图像分割中,试验结果表明,该方法不仅能够避免陷入局部极值,而且其速度得到了明显的改善,是一种有效的图像分割新方法。     

A hybrid membrane evolutionary algorithm for solving constrained optimization problems

Solving constrained optimization problems (COPs) is a central research topic in the field of optimization. Given the complexity of COPs, it is difficult to solve them with traditional optimization techniques. In this paper, a hybrid membrane evolutionary algorithm (HMEA) is proposed. It combines a one-level membrane structure with a particle swarm optimization (PSO) local search algorithm. The simulation results show that the proposed algorithm is valid and outperforms the state-of-the-art algorithms.     

基于多算法优化SVM的粗糙面参数反演

支持向量机（SVM）是粗糙面参数反演中常用的一种反演算法,SVM反演中的惩罚参数C和核函数参数G对反演结果精度的影响较大,若参数取值不当,会使模型产生"过学习"或者"欠学习"的现象,从而降低预测精度.给出几种SVM参数C和参数G的优化算法,如K折交叉验证（K-CV）、遗传算法（GA）和粒子群算法（PSO）,并在此基础上提出一种基于K-CV和GA改进的PSO算法（GA-CV-PSO）.利用矩量法（MoM）获得的粗糙面后向散射系数构造训练集和测试集,通过不同参数反演的仿真结果对比不同优化算法的反演精度和计算时间,表明GA-CV-PSO算法克服了单一优化算法的缺陷,具有更精确的反演精度和更强的泛化能力.     

A copyright protection watermarking algorithm for remote sensing image based on binary image watermark

As the internet keeping developing, copyright protection of the remote sensing image has become more and more important. This paper designs the algorithm that protects remote sensing image's copyright by using the binary digital watermark technology, and analyzes security and imperceptibility of the algorithm. As the experiment result shows, the algorithm put forward in this paper has better security, imperceptibility and anti-attack robustness, and thus it can meet the requirements in protecting copyright of the digital remote sensing image in an effective manner.     

嵌入式粒子群-遗传算法的水质COD检测特征波长优化算法

基于紫外-可见光谱法的水质测量中,光谱信号易受到系统噪声干扰、悬浮物散射干扰,且存在信息冗余、多重共线性等特征,导致水质COD测量中特征波长的选取产生较大偏差。因此,提出了基于嵌入式粒子群-遗传（EPSO_GA）算法的水质COD检测特征波长优化算法,以提高波长选择精度。为验证检测特征波长优化算法的可行性,采集了某高校池塘水样、生活污水和排水沟水样的光谱数据,利用EPSO_GA算法对预处理后的光谱数据选取特征波长。EPSO_GA算法采用实数编码方法实现了粒子群（PSO）优化算法和遗传（GA）优化算法的统一编码,在PSO算法中更新粒子时嵌入GA算法的选择、交叉、变异等操作,改善了这两种算法各自在光谱波长特征选取问题上的局限性。将EPSO_GA算法选取的特征波长结合偏最小二乘法（PLS）构建了EPSO_GA_PLS的水质COD预测模型,并且与传统的PSO算法、GA算法选取特征波长建立的PSO_PLS、GA_PLS和全光谱构建的PLS水质COD预测模型做了对比。结果表明：与PSO_PLS,GA_PLS和全光谱构建的PLS水质COD预测模型相比,EPSO_GA改善了PSO算法和GA算法在光谱特征波长选择中早熟和收敛速度慢的问题,降低了全光谱构建PLS水质COD预测模型的复杂度,提高了模型的预测精度。基于EPSO_GA算法建立的EPSO_GA_PLS水质COD预测模型,均方根误差降到了0.212 3,预测精度增加到0.999 3,可以快速定量检测水质COD,为紫外-可见光谱法测COD提供了更好的预测模型。     

一种基于粒子群优化的高斯混合灰度图像增强算法

提出一种采用粒子群优化(PSO)的高斯混合灰度图像增强算法。该算法首先采用高斯混合模型(GMM)对输入图像的灰度直方图建模,并采用模型中高斯成分的有效交点来分割直方图。随后,该算法将每个直方图区间的灰度值转换到合适的输出区间,生成增强后的灰度图像,其中转换函数由输入直方图区间的高斯成分和累积分布经过粒子群优化后的参数决定。实验结果显示,该方法生成的图像视觉效果较好,对原图像和纹理细节丰富图像分别进行图像增强,增强后的图像信息熵分别是4.746 6和7.952 6,灰度平均梯度为6.970 6和37.386 1。     

Image watermarking based on an iterative phase retrieval algorithm and sine-cosine modulation in the discrete-cosine-transform domain

A novel digital image watermarking system based on an iterative phase retrieval algorithm and sine-cosine modulation in the discrete-cosine-transform (DCT) domain is proposed. The original hidden image is first encrypted into two phase masks. Then the cosine and sine functions of one of the phase masks are introduced as a watermark to be embedded into an enlarged host image in the DCT domain. By extracting the watermark of the enlarged superposed image and decryption we can retrieve the hidden image. The feasibility of this method and its robustness against some attacks, such as occlusion, noise attacks, quantization have been verified by computer simulations. This approach can avoid the cross-talk noise due to direct information superposition and enhance the imperceptibility of hidden data.