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This paper introduces an efficient and scalable cloud-based privacy preserving model using a new optimal cryptography scheme for anomaly detection in large-scale sensor data. Our proposed privacy preserving model has maintained a better tradeoff between reliability and scalability of the cloud computing resources by means of detecting anomalies from the encrypted data. Conventional data analysis methods have used complex and large numerical computations for the anomaly detection. Also, a single key used by the symmetric key cryptographic scheme to encrypt and decrypt the data has faced large computational complexity because the multiple users can access the original data simultaneously using this single shared secret key. Hence, a classical public key encryption technique called RSA is adopted to perform encryption and decryption of secure data using different key pairs. Furthermore, the random generation of public keys in RSA is controlled in the proposed cloud-based privacy preserving model through optimizing a public key using a new hybrid local pollination-based grey wolf optimizer (LPGWO) algorithm. For ease of convenience, a single private server handling the organization data within a collaborative public cloud data center when requiring the decryption of secure sensor data are allowed to decrypt the optimal secure data using LPGWO-based RSA optimal cryptographic scheme. The data encrypted using an optimal cryptographic scheme are then encouraged to perform data clustering computations in collaborative public servers of cloud platform using Neutrosophic c-Means Clustering (NCM) algorithm. Hence, this NCM algorithm mainly focuses for data partitioning and classification of anomalies. Experimental validation was conducted using four datasets obtained from Intel laboratory having publicly available sensor data. The experimental outcomes have proved the efficiency of the proposed framework in providing data privacy with high anomaly detection accuracy.

Carrying out an engineering process with the least cost and within the shortest time is the basic purpose in many fields of industry. In Computer Numerical Control (CNC) machining, performing a process by following a certain order reduces cost and time of the process. In the literature, there are research works involving varying methods that aim to minimize the length of the CNC machine tool path. In this study, the trajectory that the CNC machine tool follows while drilling holes on a plate was discussed within the Travelling Salesman Problem (TSP). Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Grey Wolf Optimizer (GWO) methods were used to solve TSP. The case that the shortest tool path was obtained was determined by changing population size parameter in GA, PSO, and GWO methods. The results were presented in tables.

In recent days, study on retinal image remains a significant area for analysis. Several retinal diseases are identified by examining the differences occurring in the retina. Anyhow, the major shortcoming between these analyses was that the identification accuracy is not satisfactory. The adopted framework includes two phases namely; (i) feature extraction and (ii) classification. Initially, the input fundus image is subjected to the feature extraction process, where the features like Local Binary Pattern (LBP), Local Vector Pattern (LVP) and Local Tetra Patterns (LTrP) are extracted. These extracted features are subjected to the classification process, where the Deep Belief Network (DBN) is used as the classifier. In addition, to improve the accuracy, the activation function and hidden neurons of DBN are optimally tuned by means of the Self Improved Grey Wolf Optimization (SI-GWO). Finally, the performance of implemented work is compared and proved over the conventional models.

Convolutional Neural Network (CNN) has shown remarkable success in the area of machine vision. The purpose of this research is to enhance the classification for the few-shot learning datasets by developing a robust feature extraction system using an optimized CNN model. The aforementioned goal is attained in the following way by developing two classification models, (1) CNN optimized using Two-Dimensional Discrete Wavelet Transform (2D-DWT) injection using Principal Component Analysis (PCA), and Grey Wolf Optimizer (GWO), and (2) CNN optimized using 2D-DWT injection using PCA and Multi-Verse Optimizer (MVO) algorithm. This optimization process enhances the rate of face recognition for the small training dataset by extracting maximum features. Experiments on the AT&T (ORL), LFW, and Extended Yale-FACE-B databases show that the technique improves results significantly, with recognition rates increasing to 100% for training accuracy on all datasets and 100%, 98%, 97% on ORL, LFW, and Extended Yale-FACE-B datasets, respectively, for testing accuracy.

To overcome the poor population diversity and slow convergence rate of grey wolf optimizer (GWO), this paper introduces the elite opposition-based learning strategy and simplex method into GWO, and proposes a hybrid grey optimizer using elite opposition (EOGWO). The diversity of grey wolf population is increased and exploration ability is improved. The experiment results of 13 standard benchmark functions indicate that the proposed algorithm has strong global and local search ability, quick convergence rate and high accuracy. EOGWO is also effective and feasible in both low-dimensional and high-dimensional case. Compared to particle swarm optimization with chaotic search (CLSPSO), gravitational search algorithm (GSA), flower pollination algorithm (FPA), cuckoo search (CS) and bat algorithm (BA), the proposed algorithm shows a better optimization performance and robustness.

In this paper, we propose a new hybrid population-based meta-heuristics algorithm inspired by grey wolves in order to solve integer programming and minimax problems. The proposed algorithm is called Multidirectional Grey Wolf Optimizer (MDGWO) algorithm. In the proposed algorithm, we try to accelerate the standard grey wolf optimizer algorithm (GWO) by invoking the multidirectional search method with it in order to accelerate the search instead of letting the standard GWO run for more iterations without significant improvement in the results. MDGWO starts the search by applying the standard GWO search for a number of iterations, and then the best-obtained solution is passed to the multidirectional search method as an intensification process in order to accelerate the search and overcome the slow convergence of the standard GWO algorithm. We test MDGWO algorithm on seven integer programming problems and 10 minimax problems. Moreover, we compare against 11 algorithms for solving integer programming problems and 10 algorithms for solving minimax problems. Furthermore, we show the efficiency of the proposed algorithm and its ability to solve integer and minimax optimization problems in reasonable time by giving several results of the experiments.

In this paper, a Sugeno type fuzzy system based on the fuzzy clustering has been developed for a variety of datasets. The number of rules for each dataset is based on the optimum number of clusters in that dataset. Rule sets provide the knowledge base for the classification of data. Each rule set is fine-tuned using the GWO with the intention to improve the classification. The approach is compared with the work of previous researchers on similar data sets using a variety of techniques, including nature-inspired algorithms such as genetic algorithms and Swarm based algorithms. Statistical Analysis of the performance of GWO shows that it is better than five other algorithms 95% of the time.

Grey Wolf optimizer (GWO) has been used in several fields of research. The main advantages of this algorithm are its simplicity, little controlling parameter, and adaptive exploratory behavior. However, similar to other metaheuristic algorithms, the GWO algorithm has several limitations. The main drawback of the GWO algorithm is its low capability to handle a multimodal search landscape. This drawback occurs because the alpha, beta, and gamma wolves tend to converge to the same solution. This paper presents HDGM – a novel hybrid optimization model of dragonfly algorithm and grey wolf optimizer, aiming to overcome the disadvantages of GWO algorithm. Dragonfly algorithm (DA) is combined with GWO in this study because DA has superior exploration ability, which allows it to search in promising areas in the search space. To verify the solution quality and performance of the HDGM algorithm, we used twenty-three test functions to compare the proposed model’s performance with that of the GWO, DA, particle swam optimization (PSO) and ant lion optimization (ALO). The results show that the hybrid algorithm provides more competitive results than the other variants in terms of solution quality, stability, and capacity to discover the global optimum.

One of the well-known issues in the field of network routing is the Shortest Path Routing (SPR). The objective is to find the least-cost path with minimum delay and link breaks. Even though there are many algorithms to solve SPR, the cost, as well as link breaks, are indeed more thought-provoking in the real-time application. This paper intends to develop a routing approach that solves the challenges like route establishment and route recovery. The selection of the optimal route is done by adopting a generalized multi-purpose optimization algorithm named Grey Wolf Optimizer. Along with this, this paper adopts Neural Network (NN) to predict the node movements in the ad hoc network. The proposed routing algorithm is compared to the conventional approaches, and the significance of the approach is described clearly.

The technical advancement in information systems contributes towards the massive availability of the documents stored in the electronic databases such as e-mails, internet and web pages. Therefore, it becomes a complex task for arranging and browsing the required document. This paper proposes an approach for incremental clustering using the Bat-Grey Wolf Optimizer (BAGWO). The input documents are initially subjected to the pre-processing module to obtain useful keywords, and then the feature extraction is performed based on wordnet features. After feature extraction, feature selection is carried out using entropy function. Subsequently, the clustering is done using the proposed BAGWO algorithm. The BAGWO algorithm is designed by integrating the Bat Algorithm (BA) and Grey Wolf Optimizer (GWO) for generating the different clusters of text documents. Hence, the clustering is determined using the BAGWO algorithm, yielding the group of clusters. On the other side, upon the arrival of a new document, the same steps of pre-processing and feature extraction are performed. Based on the features of the test document, the mapping is done between the features of the test document, and the clusters obtained by the proposed BAGWO approach. The mapping is performed using the kernel-based deep point distance and once the mapping terminated, the representatives are updated based on the fuzzy-based representative update. The performance of the developed BAGWO outperformed the existing techniques in terms of clustering accuracy, Jaccard coefficient, and rand coefficient with maximal values 0.948, 0.968, and 0.969, respectively.