black widow optimization algorithm

The Internet of Things (IoT) now connects over nine billion devices. This number is predicted to approach 20 billion in the near future, and the number of things is rapidly expanding, implying that a large amount of data will be created. To handle the connected things, an infrastructure must be built. Cloud computing (CC) has become necessary in the analysis and data storage for IoT. A cloud broker, which is an intermediate in the infrastructure that controls connected things in cloud computing, is discussed in this study. An optimization problem is examined for maximizing the broker's profit and system availability while minimizing request response time and energy consumption. For this purpose, an objective function is proposed and solved using the Black Widow Optimization (BWO) algorithm. Subsequently, the obtained results are compared with the particle swarm optimization (PSO) algorithms. The results indicate that the BWO algorithm could outperform the PSO algorithm, and it can provide much better results considering different scenarios.

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.