## 1 Introduction  
The proliferation of smart cities (SC), smart security, and smart community have made anomalous behavior analysis a hot topic in crowd event research. Security is a highly significant component in city; it becomes vital to ensure a safe ecosystem for the data that is produced and for the people [1]. Cities should enforce security measures for guaranteeing complete security of the individuals' information, and the information produced by the sensors and urban infrastructure, among others. In the present era, it is very significant to enable compliance with precautionary measures that safeguard people; for instance, if they have adequate social distancing measures in public places [2]. In this context, imaginative systems that enable us to find those places where there are less pedestriansis of considerable significance, in order to simplify the leisure of the family who need to go out [3]. The SC construction is a dynamic procedure, and the management platform should always be ready not only for ingesting the real-time dataset, but for incorporating the information from various sources and bringing about these data for analysis, along with creating various visualization approaches and integrating several data [4]. Currently, intelligent monitoring is increasingly implemented in common places (shopping malls, hospitals, and campuses) [5]. Of these, the crowding degree is the main problem that influences the study of the abnormal performance of crowds. It can be realized by crowd counting and crowd density (CD) estimation. CD prediction allures the interest of researchers abroad and at home in increasing numbers [5]. Currently, researchers abroad and at home have performed research relating to crowd abnormality with the focus on track path abnormality, crowd violence, crowd crowding, and crowd panic. The density and count of the crowd were frequently employed to reflect the degree of crowd crowding. There are two types of crowd analysis which rely upon crowd density prediction and crowd count. Crowd density prediction is assessing the crowd dispersion and specific groups of people [6]. Crowd analysis is gaining considerable attention among scholars in recent times due to several factors. The massive growth in urbanization and the global population has led to a rise in events such as political rallies, public demonstrations, and sporting events, among others. Similar to other computer vision (CV) problems, crowd analysis will face several complexities such as an uneven distribution of people, inter-scene variations in appearance, high clutter, non-uniform illumination, an unclear viewpoint, intra-scene and scale issues, and occlusions; such issues are very tough to solve. The uncertainty of the problem in addition to the wide array of applications for analyzing crowds resulted in an augmented focus among researchers in recent times. Convolution neural network (CNN) achieved fruitful results in image processing and in CD estimation. On the other hand, the hyperparameters of the DL models play a vital role in attaining enhanced performance. Owing to continual deepening of the model, the number of parameters of DL models also increases quickly which results in model overfitting [7]. At the same time, different hyperparameters have a significant impact on the efficiency of the CNN model. Particularly, the hyperparameters such as epoch count, batch size, and learning rate selection are essential to attain an effectual outcome. The trial-and-error method for hyperparameter tuning is a tedious and erroneous process; metaheuristic algorithms can be applied. The hyperparameter tuning can be considered an NP hard problem which can be solved using metaheuristic algorithms such as genetic algorithm, hunger games search, and memetic algorithm, among others [8]. The metaheuristic is a high-level problem-independent algorithmic model which offers a collection of strategies to design heuristic optimization algorithms [9; 10].