# Metaheuristics Optimization with Deep Learning Enabled Automated Image Captioning System
## 1 Introduction  
Presently, a significant number of images have been produced from many origins such as advertisements, the internet, document diagrams, and news articles. Such origins have images which viewers should analyze themselves [1]. Many images do not contain descriptions; however, human beings can mostly understand them without having any detailed captions. However, machinery should make an interpretation in certain forms of image captions whenever human beings require automated image captions from it. Image captioning is considered significant on numerous grounds [2]. For instance, it is utilized for automated image indexing. Image indexing plays a vital role in content-based imageretrieval (CBIR), and thus is implemented in numerous areas involving digital libraries, biomedicine, the military, education, web searching, and commerce. Mass media platforms such as Twitter and Facebook could straight away produce descriptions from images [3]. The descriptions might involve places (e.g., beach, cafe), things that are worn, and, most significantly, the activities that are taking place. Image captioning basically involves natural language processing (NLP) and computer vision. Computer vision is helpful in recognizing and understanding the situation in an image [4]; NLP transforms semantic knowledge into a descriptive line. Retrieval of the semantic matter of an image and communicating it in a structure which human beings can understand becomes extremely complex. The complete image captioning method not only gives information, but it also further reveals the connection among the substances [5]. Image captioning consists of numerous applications--for example, as an aid advanced for guiding the person having visual disabilities while traveling alone [6]. This is made possible by changing the scenario into text and transforming the text into voice messages. Image captioning is also utilizes mass communication for the automatic generation of the caption for an image which is posted or to explain a video [7,8]. Moreover, automated image captioning might enhance the Google image search method by changing the image to a caption and, after, by utilizing the keywords for additional related searches [9]. Image realization mostly relies on acquiring image features. The methods utilized for understanding the image of two categories are deep machine learning (ML)-related methods and old machine learning-related methods (2). In conventional ML, handcrafted features, namely the histogram of oriented gradients (HOG), local binary patterns (LBPs), and scale-invariant feature transform (SIFT), and a grouping of these features, were broadly utilized. In such methods, features have been derived from the input unit [10]. They were passed afterward to a classifier such as support vector machines (SVMs) for article classification. Furthermore, real-world data such as video and images become complicated and contain diverse semantic interpretations [11]. Conversely, in ML-related methods, features were studied automatically from training data, and they could manage a big and varied set of videos and images. For instance, convolutional neural networks (CNNs) were broadly employed for feature learning, and a classifier such as Softmax can be utilized for categorization. CNN can be usually tracked by recurrent neural networks (RNNs) for generating captions [12]. This study develops a metaheuristic optimization with a deep learning-enabled automated image captioning technique (MODLE-AICT). The proposed MODLE-AICT model aims for the generation of effective captions to the input images by using two processes involving an encoding unit and a decoding unit. At the encoding part, the salp swarm algorithm (SSA) with a HybridNet model is utilized to generate effectual input image representation using fixed-length vectors. Then, the decoding part includes a bidirectional gated recurrent unit (BiGRU) model to generate descriptive sentences.