whale optimization algorithm (woa)

The surge in internet usage has sparked new demands. Historically, specialized web crawlers were devised to retrieve pages pertaining to specific subjects. However, contemporary needs such as event identification and extraction have gained significance. Conventional web crawlers prove inadequate for these tasks, necessitating exploration of novel techniques for event identification, extraction, and utilization. This study presents an innovative approach for detecting and extracting events using the Whale Optimization Algorithm (WOA) for feature extraction and classification. By integrating this method with machine learning algorithms, the proposed technique exhibits improvements in experiments, including decreased execution time and enhancements in metrics such as Root Mean Square Error (RMSE) and accuracy score. Comparative analysis reveals that the proposed method outperformed alternative models. Nevertheless, when tested across various data models and datasets, the WOA model consistently demonstrated superior performance, albeit exhibiting reduced evaluation metrics for Wikipedia text data.

Fog computing is known as a new computing technology where it covers cloud computing shortcomings in term of delay. This is a potential for running IoT applications containing multiple services taking benefit of closeness to fog nodes near to devices where the data are sensed. This article formulates service placement issue into an optimization problem with total power consumption minimization inclination. It considers resource utilization and traffic transmission between different services as two prominent factors of power consumption, once they are placed on different fog nodes. On the other hand, placing all of the services on the single fog node owing to power reduction reduces system reliability because of one point of failure phenomenon. In the proposed optimization model, reliability limitations are considered as constraints of stated problem. To solve this combinatorial problem, an energy-aware reliable service placement algorithm based on whale optimization algorithm (ER-SPA-WOA) is proposed. The suggested algorithm was validated in different circumstances. The results reported from simulations prove the dominance of proposed algorithm in comparison with counterpart state-of-the-arts.