ABSTRACT Transfer functions have a very important role in metaheuristic optimization‐based feature selection algorithms as these functions map the continuous search space into binary space. The U‐shaped transfer function (UTF) is one of the transfer functions used to solve the problem of feature selection. However, the UTF requires the selection of parametric values, which can vary for different types of data. To address this issue, an approach to select the parameters of the UTF has been proposed based on a time‐varying adaption method, resulting in the modified U‐shaped transfer function (MUTF). Furthermore, a methodology has been proposed to enhance feature selection and classification for Parkinson's disease by utilizing z‐score normalization in conjunction with a modified U‐shaped transfer function and the binary self‐adaptive bald eagle search (MUTF‐SABES) optimization algorithm. The z‐score normalization has been used to mitigate issues caused by outliers. Also, the performance of the k nearest neighbor classifier is improved by selecting an optimal parameter value using the proposed MUTF‐SABES algorithm. The effectiveness of the proposed methodology is validated on seven different Parkinson's disease datasets and compared with five state‐of‐the‐art optimization algorithms: Salp Swarm algorithm, Harris Hawks optimization, equilibrium optimizer, aquilla optimizer, and Honey Badger algorithm, to evaluate its performance superiority. The results achieved using the proposed approach have been superior or analogous to the erstwhile algorithms for performance comparability. Friedman's mean rank test is used to check the statistical significance of the propounded approach. The lowest Friedman's mean rank value obtained using the proposed approach indicates that the proposed approach has the potential to become an alternative to other well‐known strategies.

Summary The vast technological developments make data transmission more frequent over networks. So, the data needs to be secured and for that reason, there is a requirement to develop an effective encryption model. This article proposes a novel image encryption model using multi‐chaotic maps and blockchain (MCBE). Chaotic maps have been used in encryption models as chaotic maps have the properties of randomness, and non‐periodicity that get utilized in improving the efficiency of an encryption model. Logistic and tent maps have been employed as multi‐chaotic maps to make the encryption process effective in this work. A logistic map has a simple structure and is easily implementable but has some drawbacks like small keyspace. Keyspace and randomness are enhanced by using a tent map along with a logistic map. The proposed MCBE methodology applies to grayscale or colored images having different file formats and sizes. Initially, in the confusion phase, the permutation of the original image is performed based on a random permutation that changes the original image pixel's position. The encryption model applies multi‐chaotic maps, row‐wise and column‐wise in the diffusion phase to promote the efficiency of the encryption model. Finally, blockchain has been implemented using the SHA‐256 hash function to obtain the encrypted image that enhances the security by increasing the keyspace, to resist brute force attacks. The evaluation performance of the MCBE has been analyzed against various attacks namely statistical attacks, differential attacks, NIST randomness test, noise, and cropping attacks. The evaluated keyspace is which has high key sensitivity with a correlation of encrypted images close to 0. Maximum achieved entropy value of 7.9998, SSIM value between original and encrypted images nearby 0 confirms the dissimilarity. The Number of Pixels Change Rate (NPCR) value of 99.62%, and the value of Unified Average Changed Intensity (UACI) value of 33.54% are within the specified standard range. The calculated correlation coefficient, NPCR, and UACI values have been compared with the existing algorithms, and the results show that the proposed MCBE methodology has better performance than the other state‐of‐the‐art methods. The experimental results indicate that the chaotic ranges generated by multi‐chaotic maps and the SHA‐256 hash function improve the keyspace and security of the encryption model confirming the efficacy of the proposed model MCBE.

Summary Metaheuristic techniques have gained the attention of many researchers in the last few years. These techniques are used to solve real‐world optimization problems as well as for feature selection. This work proposes a lévy flight and disrupt operator‐based elephant herding optimization algorithm (LDEHO). In the proposed algorithm, opposition based‐learning is utilized to commence with better initial positions. Lévy flight and matriarch mean are introduced to update the positions of clan individuals. In addition, the disruption phenomenon is introduced to generate new individuals for replacing the worst clan individuals. Further, an elitism scheme is introduced to preserve the best search agents in consecutive iterations. The performance of the proposed LDEHO algorithm is validated on 97 benchmark functions. A comparative analysis of the proposed LDEHO algorithm with fourteen state‐of‐the‐art algorithms has been made. Results show the high potential of the LDEHO algorithm in solving the benchmark functions. Further, Friedman's mean rank test and multiple comparison tests are applied to demonstrate the statistically significant difference between the algorithms. Moreover, a binary version of the proposed LDEHO algorithm is introduced for feature selection to classify the medical datasets. The performance of the binary LDEHO is validated on 15 medical datasets and compared with six state‐of‐the‐art algorithms. Results show the supremacy of the binary LDEHO for feature selection to classify the medical data. Friedman's test proves that the proposed binary LDEHO algorithm is statistically different and better than other algorithms.

Abstract Bald Eagle Search optimization (BES) is introduced recently, which mimics the bald eagles' hunting and food searching behaviour. The capability of a BES algorithm is enhanced in this paper by avoiding local optima stagnation and premature convergence problems. The BES algorithm is modified to enhance the performance of the algorithm and the modified algorithm is called the Self Adaptive Bald Eagle Search (SABES) algorithm. Dynamic‐opposite Learning (DOL) method is invoked in the initialization phase to increase the population diversity and convergence speed. To find better global solutions, the exploitation capability of the SBES algorithm is enhanced by considering the dynamic‐opposite solutions. In addition, the algorithmic parameter values of the BES algorithm have been determined using the linear and non‐linear time‐varying adaption strategy to create a balance between the search abilities which promotes the overall performance of the algorithm. The performance of the SABES algorithm is validated by comparing the results of 50 benchmark functions and CEC2017 functions with different erstwhile algorithms. The proposed algorithm achieves the best results in 80% of the benchmark functions, whereas the BES only gets the best results in 56% of functions. For the CEC2017, the SABES algorithm achieves optimal results for 20 functions which are highest in comparison to state‐of‐the‐art algorithms. The feasibility and effectiveness of the proposed algorithm are checked using the 15 CEC2020 competition real‐world single‐objective constrained optimization problems. The SABES achieves a 100% success rate and 100% feasibility rate in comparison to other well‐regarded algorithms. The statistical significance of the algorithm has been proved using Friedman's mean rank and Wilcoxon sign rank test.

The response to a natural disaster ultimately depends on credible and real-time information regarding impacted people and areas. Nowadays, social media platforms such as Twitter have emerged as the primary and fastest means of disseminating information. Due to the massive, imprecise, and redundant information on Twitter, efficient automatic sentiment analysis (SA) plays a crucial role in enhancing disaster response. This paper proposes a novel methodology to efficiently perform SA of Twitter data during a natural disaster. The tweets during a natural calamity are biased toward the negative polarity, producing imbalanced data. The proposed methodology has reduced the misclassification of minority class samples through the adaptive synthetic sampling technique. A binary modified equilibrium optimizer has been used to remove irrelevant and redundant features. The k-nearest neighbor has been used for sentiment classification with the optimized value of k. The nine datasets on natural disasters have been used for evaluation. The performance of the proposed methodology has been validated using the Friedman mean rank test against nine state-of-the-art techniques, including two optimized, one transfer learning, one deep learning, two ensemble learning, and three baseline classifiers. The results show the significance of the proposed methodology through the average improvement of 6.9%, 13.3%, 20.2%, and 18% for accuracy, precision, recall, and F1-score, respectively, as compared to nine state-of-the-art techniques.

Abstract In this article, a hybrid algorithm has been proposed for the identification of phishing and legitimate websites. The dataset may have an imbalanced class distribution and may consist of irrelevant features. Therefore, in the data preprocessing, the adaptive synthetic sampling approach has been used to handle the imbalanced data. Irrelevant or redundant features are removed from the balanced data using the proposed binary version of Rao algorithms. The S‐shaped and V‐shaped transfer functions are applied for mapping continuous search space to discrete search space. Also, the results of these S‐shaped and V‐shaped transfer functions are analyzed for proposed algorithms. The performance is improved by optimizing the value of the k parameter in the kNN classifier. The dataset used in this article has been taken from the UCI machine‐learning repository. The performance of the proposed approach has been evaluated using the polygon area metric. The obtained classification accuracy is 97.044%. A comparison of the proposed hybrid algorithm with the other state‐of‐the‐art techniques is also made for validation. Moreover, the proposed approach has been compared with seven metaheuristic feature selection algorithms and six filter methods for performance analysis. Additionally, we have applied the proposed approach to URLs that are registered on the PhishTank website.

Abstract Equilibrium optimizer (EO) is a novel optimization algorithm with high exploration and exploitation capabilities. The capabilities of EO are impacted by the generation rate, exponential term, and equilibrium pool, wherein the initial two depend on the turnover rate. The performance of the EO on different optimization functions can be improved by updating these factors. This paper designs a modified equilibrium optimizer (MEO) by incorporating three changes in the EO: (i) replacement of the equilibrium pool with an iterative cosine operator (ICO) that gradually reduces diversification to intensification as the iterations progress; (ii) implementation of levy flight to update the concentration of particle that improves exploration capabilities; and (iii) the random vector with uniform distribution of turnover rate is replaced by heavy‐tailed non‐uniform levy distribution to improve exploration capability of the algorithm. Here, the other parameters on which MEO depends are selected by performing the sensitivity analysis. The capabilities of MEO have been analysed by comparing it with 19 algorithms, including the EO algorithm, 12 state‐of‐art algorithms, and 6 hybrid/improved algorithms on 62 functions, that is, 23 benchmark functions, 10 CEC‐06‐2019, 29 CEC‐2017 functions. The non‐parametric Friedman test and Kruskal–Wallis test validate that MEO outperforms the other algorithms due to its balanced exploration and exploitation abilities. MEO robustness has been validated by the diversity analysis along with the scalability test. MEO is also evaluated on five practical engineering problems to showcase its significance.

Abstract Machine learning algorithms are widely applied to biomedical data to classify the samples of patients and healthy persons. The high‐dimensional biomedical datasets contain a large number of features to represent a sample. However, such datasets may have redundant, noisy and irrelevant features, influencing machine learning algorithms' classification performance and increasing computation overhead. Therefore, data normalization and feature selection techniques are introduced to reduce the impact of noisy features and accurately identify the patterns in features to improve the predictive accuracy of diagnosis. This work proposes an efficient feature selection and parameter optimization method to classify high‐dimensional biomedical datasets. In the proposed method, a binary version of the improved elephant herding optimization (IEHO) algorithm is introduced to select features and optimize the C and γ parameters of the support vector machine classifier. Further, four variants of the proposed method are presented based on data normalization techniques: Z ‐score normalization (ZN), Pareto‐scaling (PS), tan h ‐based normalization (TN), and variant of tan h ‐based normalization (VTN). The proposed variants reduce the dominance of noisy features and explore the feature space to obtain the optimal feature set that maximizes the classification accuracy and minimizes the time complexity. The performance of the proposed variants is evaluated on 15 high‐dimensional biomedical datasets. Friedman's mean rank test is applied to check the statistical difference between proposed variants. Results show that the proposed Z ‐score normalization‐IEHO (ZN‐IEHO) variant performed significantly better than the other proposed variants for classification accuracy, false‐positive rate and f ‐score metrics. Moreover, the performance of the proposed ZN‐IEHO variant is compared with 18 state‐of‐the‐art feature selection methods. The experimental results expressed the effectiveness of the proposed ZN‐IEHO variant in finding the best combination of features and parameters to classify the biomedical datasets accurately.

Abstract Rao algorithms are recently proposed optimization algorithms used to solve optimization problems. These algorithms are based on the best and the worst solutions, which are computed during the optimization process. However, these algorithms apply to continuous problems only. In this article, the binary versions of Rao algorithms are proposed, which can be used for solving feature selection problems. These are applied to four publicly available Parkinson's disease datasets. Besides providing an optimal set of features, the k parameter of the k‐nearest neighbour classifier is also optimized by the proposed approach. The performance of these algorithms has been measured taking an average of 30 independent runs using a 10‐fold cross‐validation procedure. Also, a comparison of the performance has been made with the other state of the art methods. Significance analysis of these algorithms has been made with the Friedman rank test.