Gravitational Search Algorithm (GSA) is one of the recent nature inspired algorithms which is capable to solve optimization problems. GSA is inspired by the Newtonian's law of gravity and the law of motion. The aim of the paper is to investigate GSA utilization in various optimization problems. The paper provides a brief explanation on GSA and also presents the previous optimization works based on GSA. Based on the literature, GSA is capable of providing more accurate, effective and robust high-quality solution for most of the optimization problems. The algorithm has been applied in various applications and has solved various optimization problems such as in power system, controller design, network routing, sensor networks, software design and many more. GSA has been adapted in the optimization of parameters, settings, strategies, cost, voltage control and also power dispatch. The algorithm also has been adapted to optimize the design of controllers, software, antenna and microgrids. Based on previous works, GSA has showed better performance in solving the optimization problems compared to other previous algorithms such as PSO, ACO and ABC. It is expected that more studies are to be done based on GSA in future as the algorithm has a high potential to solve various optimization problems in different areas.

The COVID-19 pandemic has such a significant impact and causes difficulties in many aspects that the new normal rules should be implemented to reduce the effects. New normal rules have been implemented by governments worldwide to break the virus chain and stop its transmission among the society. Even if the COVID-19 outbreak is under control, governments still need to know whether society could adapt and adjust to their new daily lifestyles. Many precautions still must be addressed as the transition to endemic status does not mean that COVID-19 will naturally eventually disappear. The World Health Organization also has warned that it is too early to treat COVID-19 as an endemic disease. Since the pandemic, many interactions have been done online, leading to the increasing social media usage to express opinions about COVID-19. The objective of the study is to explore the capability of the Naïve Bayes algorithm in the sentiment classification of the public's acceptance on the new normal in the COVID-19 pandemic. Naïve Bayes has been chosen for its good performance in solving various other classification problems. In this study, Twitter data were used for the analysis and were collected between March and June 2022. The evaluation results have shown that Naïve Bayes could generate excellent and acceptable performance in the classification with an accuracy of 83%. According to the findings of this research, many people have accepted the new normal in their daily lives. The future works would include scrapping more data based on geolocation, improving the feature extraction technique, balancing the dataset and comparing Naïve Bayes performance with other well-known classifiers. The subsequent study could also focus on detecting the emotions of the public and processing non-English tweets.

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