Cloud computing, with its immense potentials in low cost and on-demand services, is a promising computing platform for both commercial and non-commercial computation applications. It focuses on the sharing of information and computation in a large network that are quite likely to be owned by geographically disbursed different venders. Energy efficiency in data centers has become a hot topic in recent years as more and larger data centers have been established and the electricity cost has become a major expense for operating them. Server consolidation using virtualization technology has become an important technology to improve the energy efficiency of data centers. Virtual machine placement is the key in server consolidation. In the past few years, many approaches to virtual machine placement have been proposed, but existing virtual machine placement approaches to the virtual machine placement problem consider the energy consumption by physical machines. In this paper, we proposed a new approach for placement based on Discrete Chaotic whale optimization Algorithm. First goal of our presented algorithm is reducing the energy consumption in datacenters by decreasing the number of active physical machines. Second goal is decreasing waste of resources and management of them using optimal placement of virtual machines on physical machines in cloud data centers. By using the method, the increase in migration of virtual machines to physical machines is prevented. Finally, our proposed algorithm is compared to some algorithms in this area like FF, ACO, MGGA, GSA, and FCFS. Manuscript profile

In recent years, social networks' growth has led to an increase in these networks' content. Therefore, text mining methods became important. As part of text mining, Sentiment analysis means finding the author's perspective on a particular topic. Social networks allow users to express their opinions and use others' opinions in other people's opinions to make decisions. Since the comments are in the form of text and reading them is time-consuming. Therefore, it is essential to provide methods that can provide us with this knowledge usefully. Black Widow Optimization (BWO) is inspired by black widow spiders' unique mating behavior. This method involves an exclusive stage, namely, cannibalism. For this reason, at this stage, species with an inappropriate evaluation function are removed from the circle, thus leading to premature convergence. In this paper, we first introduced the BWO algorithm into a binary algorithm to solving discrete problems. Then, to reach the optimal answer quickly, we base its inputs on the opposition. Finally, to use the algorithm in the property selection problem, which is a multi-objective problem, we convert the algorithm into a multi-objective algorithm. The 23 well-known functions were evaluated to evaluate the performance of the proposed method, and good results were obtained. Also, in evaluating the practical example, the proposed method was applied to several emotion datasets, and the results indicate that the proposed method works very well in the psychology of texts. Manuscript profile