Genetic algorithms (GA) applied to the orthogonal projection approach (OPA) for variable selection

Multivariate curve resolution (MCR) and especially the orthogonal projection approach (OPA) can be applied to spectroscopic data and were proved to be suitable for process monitoring. To improve the quality of the on-line monitoring of batch processes, it is interesting to get as many as possible spectra in a given period of time. Nevertheless, hardware limitations could lead to the fact that it is not possible to acquire more than a certain number of spectra in this given period of time. Wavelength selection could be a good way to limit this problem since it decreases size, and consequently the acquisition time, of each recorded spectrum. This paper details an industrial application of genetic algorithms (GA) coupled with a curve resolution method (OPA) for such purpose.     

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.     

基于证据推理的动态联盟伙伴选择优化模型

讨论了定性和定量相结合选择动态联盟伙伴的问题，针对权系数信息不完全和指标值不确定提出了一种基于证据推理的优化模型。该模型首先通过证据推理算法将方案的指标值集结，然后将效用值集结，结合不完全信息的权系数建立非线性规划模型，设计了遗传算法来求解，从而选出最满意的伙伴。最后以实例表明该模型的有效性。     

Reliability analysis and optimization of weighted voting systems with continuous states input

Weighted voting systems are widely used in many practical fields such as target detection, human organization, pattern recognition, etc. In this paper, a new model for weighted voting systems with continuous state inputs is formulated. We derive the analytical expression for the reliability of the entire system under certain distribution assumptions. A more general Monte Carlo algorithm is also given to numerically analyze the model and evaluate the reliability. This paper further proposes a reliability optimization problem of weighted voting systems under cost constraints. A genetic algorithm is introduced and applied as the optimization technique for the model formulated. A numerical example is then presented to illustrate the ideas.     

遗传-粒子群算法模型修正

用部分测量模态数据对5层钢架结构进行模型修正,将遗传算法、粒子群优化算法、 遗传-粒子群组合算法3种算法在该模型修正过程中的效率和精度进行比较,结果表明修正后 模型的全部四阶频率和振型都能在不同程度上向目标值靠近,证明3种算法都能够有效修正 模型,而且遗传-粒子群算法能在前期利用遗传算法进行高效全局搜索,后期利用粒子群算法 进行细致局部搜索,与单独使用遗传算法或粒子群算法相比,组合算法效率和精度更高.     

安全第一标准下基于VaR度量的投资组合优化问题

为了让证券投资者更好地按照自己的安全标准进行投资,根据PYLE H和TURNOVSKY S J提出的安全第一标准,给出在风险价值（VaR）度量下如何选取到最优证券组合的相关结果．其结论对投资者在选择证券投资组合时具有理论上的参考价值．     

连续面型高效率光栅分束器件的设计

阐述了用虚源法设计连续面型光栅分束器件的原理 ,提出了一种局部搜索遗传算法 ,并将其用于优化器件的性能。局部搜索遗传算法结合了局部搜索算法和遗传算法的优点 ,可以有效地克服遗传算法的“早熟收敛”现象 ,具有更强的全局收敛能力。用文中给出的方法可以得到具有较好均匀性的、高衍射效率的连续面型光栅分束器件。     

Optimization of passive optical network planning

Network planning problem typically involves large capital investment and can be formulated as an optimization problem where the objective is minimization of the first installed cost. We consider a passive optical network (PON) planning problem based on a residential area in Adana (Turkey). There are four possible primary node locations, twenty possible secondary node locations, and twenty-eight customers. We use genetic algorithm and mathematical modeling techniques to optimize the position of the primary and secondary nodes, their split levels and assigning customers to secondary nodes and secondary nodes to primary nodes under some constraints such as system’s attenuations and technical characteristics of all the equipment.     

Determining the Kinematic Viscosity of Lubricant Oils for Gear Motors by Using the Near Infrared Spectroscopy (NIRS) and the Wavelength Selection

This paper reports the chemometric predictive models developed for near infrared spectroscopy (NIRS) for the quantitative determination of the kinematic viscosity (37.1–93.1 cSt) of lubricant oils for gear motors. The gear motor is a complete motive force system that consists of an electric motor and a reduction gear train integrated into one easy-to-mount and configure package. The method used for measuring the viscosity of the lubricating oil was ASTM D445, the Standard Test Method for Kinematic Viscosity of Transparent and Opaque Liquids. A comparison was made among several multivariate calibration techniques and algorithms for pre-processing and variable selection of data, including partial least squares, interval partial least squares (iPLS), a genetic algorithm (GA), and a successive projections algorithm. Finally, the results obtained for the root mean square errors of prediction in cSt and relative average error were, respectively, 1.86 and 2.97% (GA) and 2.36 and 2.97% (iPLS). The method proposed in this study is a useful alternative for the determination of the kinematic viscosity in oils for gear motors.