RSO: A Novel Reinforced Swarm Optimization Algorithm for Feature Selection

Evaluating the state of health (SOH) of lithium-ion batteries (LIBs) is essential for their safe deployment and the advancement of electric vehicles (EVs). Existing machine learning methods face challenges in the automation and effectiveness of feature extraction, necessitating improved computational efficiency. To address this issue, we propose a collaborative approach integrating an enhanced whale optimization algorithm (EWOA) for feature selection and a lightweight support vector regression (SVR) model for SOH estimation. Key features are extracted from charging voltage, current, temperature, and incremental capacity (IC) curves. The EWOA selects features by initially assigning weights based on importance scores from a random forest model. Gaussian noise increases population diversity, while a dynamic threshold method optimizes the selection process, preventing local optima. The selected features construct the SVR model for SOH estimation. This method is validated using four aging datasets from the NASA database, conducting 50 prediction experiments per battery. The results indicate optimal average absolute error (MAE) and root mean square error (RMSE) within 0.41% and 0.71%, respectively, with average errors below 1% and 1.3%. This method enhances automation and accuracy in feature selection while ensuring efficient SOH estimation, providing valuable insights for practical LIB applications.

Centroid mutation-based Search and Rescue optimization algorithm for feature selection and classification

A novel quantum grasshopper optimization algorithm for feature selection

Feature selection (FS) constitutes a critical stage within the realms of machine learning and data mining, with the objective of eliminating irrelevant features while guaranteeing model accuracy. Nevertheless, in datasets featuring a multitude of features, choosing the optimal feature poses a significant challenge. This study presents an enhanced Sand Cat Swarm Optimization algorithm (MSCSO) to improve the feature selection process, augmenting the algorithm's global search capacity and convergence rate via multiple innovative strategies. Specifically, this study devised logistic chaotic mapping and lens imaging reverse learning approaches for population initialization to enhance population diversity; balanced global exploration and local development capabilities through nonlinear parameter processing; and introduced a Weibull flight strategy and triangular parade strategy to optimize individual position updates. Additionally, the Gaussian-Cauchy mutation strategy was employed to improve the algorithm's ability to overcome local optima. The experimental results demonstrate that MSCSO performs well on 65.2% of the test functions in the CEC2005 benchmark test; on the 15 datasets of UCI, MSCSO achieved the best average fitness in 93.3% of the datasets and achieved the fewest feature selections in 86.7% of the datasets while attaining the best average accuracy across 100% of the datasets, significantly outperforming other comparative algorithms.

Objectives: A novel hybrid multi-objective swarm system is proposed in order to find the optimal feature subset that achieves data description with minor redundancy as well as keeps classification performance. Methods/Statistical Analysis: The advantages of filter and wrapper approaches characteristics’ are merged simultaneously via different phases of optimization. The proposed system based on Modified Moth Flame Optimization algorithm is assessed against the original algorithm in different experimentations with a single objective based on using MFO and with a multi-objective MFO. The proposed system has been tested over 21 data sets derived from UCI data repository under set of statistical assessment indicators. Findings: The experimental results proved the capability of the hybrid multi-objective MFO-2 to adaptively search the feature space to find optimal feature subset with highest mutual information and achieves maximum classification accuracy and tolerates the problems that are common on both wrapper-based feature selection as well as filter based ones. In addition to, the results obtained from using Random Forests ensemble classifier within the proposed system proved that the performance of hybrid system based on the modified MFO-2 is superior to the hybrid system based on original MFO algorithm in 62% from the data sets, while in case of using the K-Nearest Neighbor classifier we can highlight that the proposed hybrid multi-objective MFO-2 outperforms the other single objective MFO-2 as well as the hybrid multi-objective MFO algorithm in classification performance but also has a comparable ratio of features selected, which confirms that the hybrid multi-objective MFO-2 can select the optimal feature combination with comparable size. Application/Improvements: The novel hybrid multi-objective MFO-2 proved the capability to adaptively search the feature space and its ability to avoid premature convergence caused by falling in local minima. Keywords: A Hybrid Model, Feature Selection, Filter and Wrapper Approaches, Modified Moth Flame Optimization (MFO2), Multi-Objective

Feature selection is an important part of data mining, image recognition and other fields. The efficiency and accuracy of classification algorithm can be improved by selecting the best feature subset. The classical feature selection technology has some limitations, and heuristic optimization algorithm for feature selection is an alternative method to solve these limitations and find the optimal solution. In this paper, we proposed a Multi Strategy Grey Wolf Optimizer algorithm (MSGWO) based on random guidance, local search and subgroup cooperation strategies for feature selection, which solves the problem that the traditional grey wolf optimizer algorithm (GWO) is easy to fall into local optimization with a single search strategy. Among them, the random guidance strategy can make full use of the random characteristics to enhance the global search ability of the population, and the local search strategy makes grey wolf individuals make full use of the search space around the current best solution, and the subgroup cooperation strategy is very important to balance the global search and local search of the algorithm in the iterative process. MSGWO algorithm cooperates with each other in three strategies to update the location of grey wolf individuals, and enhances the global and local search ability of grey wolf individuals. Experimental results show that MSGWO can quickly find the optimal feature combination and effectively improve the performance of the classification model.

A fast Branch-and-Bound algorithm for U-curve feature selection

With the growth of Internet, there has been a tremendous increases in the number of attacks and therefore Intrusion Detection Systems (IDS’s) has become a main stream of information security. The purpose of IDS is to help the computer systems to deal with attacks. This anomaly detection system creates a database of normal behaviour and deviations from the normal behaviour to trigger during the occurrence of intrusions. Based on the source of data, IDS is classified into Host based IDS and Network based IDS. In network based IDS, the individual packets flowing through the network are analyzed where as in host based IDS the activities on the single computer or host are analyzed. The feature selection used in IDS helps to reduce the classification time. In this paper, the IDS for detecting the attacks effectively has been proposed and implemented. For this purpose, a new feature selection algorithm called Optimal Feature Selection algorithm based on Information Gain Ratio has been proposed and implemented. This feature selection algorithm selects optimal number of features from KDD Cup dataset. In addition, two classification techniques namely Support Vector Machine and Rule Based Classification have been used for effective classification of the data set. This system is very efficient in detecting DoS attacks and effectively reduces the false alarm rate. The proposed feature selection and classification algorithms enhance the performance of the IDS in detecting the attacks.

Advances in computer technologies and internet have accumulated data at an exceptional speed. Majority of data available is in textual form and might contain billions of significant observations and thousands of features. In the era of Big data, text categorization is the primary step for handling and organizing textual data. Mining of knowledge from unstructured data is a more challenging task and proper representation of unstructured data will significantly improve the performance of the knowledge extraction process. Bag of words is a basic text representation technique, but has not been much successful due to the properties synonymy and polysemy of the English language. These problems could be reduced when documents are represented as a bag of phrases. Finding the best subset of features for a problem in a large domain becomes intractable and many such problems have been proved to be NP-Hard. Optimization algorithms are designed to approach such NP-Hard problems to find nearly optimal solutions. This chapter discusses about using MapReduce programming model for statistical feature selection when documents are represented as a bag of syntactic phrases and designing a parallel ant colony optimization algorithm for feature selection.

Individual optimal feature selection algorithm is simple and effective. Generally, single criterion is chosen for feature selection. But in this case, it is possible that features with good performance on other criteria will be neglected, and causing a negative impact to feature selection result. For the problem, the paper proposes a comprehensive evaluation model based on fuzzy correlation projection for evaluating the comprehensive indexs of individual features, and takes the results as the basis of feature selection. Definition to the model and its application procedure are described. Finally, the proposed method is applied to evaluate and select the underwater target recognition feature. The experimental result shows that the selected feature subset based on comprehensive indexs has a higher testing recognition rate than that based on single criterion, e.g. Relief-F indexs. Therefore, the proposed individual feature evaluation and selection method based on comprehensive indexs is feasible and effective.

Breast cancer is the second most frequent malignant tumor in the world. Early findings of breast cancer can significantly improve treatment effectiveness. Manual methods of breast cancer diagnosis are prone to human fault and inaccuracy, and they take time. A computer‐aided diagnosis can assist radiologists in making better choices by overcoming the disadvantages of manual methods. One of the significant steps in the breast cancer diagnosis process is feature selection. In recent decades, many studies have proposed numerous hybrid optimization methods to select the optimal features in the breast cancer detection system. However, many hybrid optimization algorithms are trapped in local optima and have slow convergence speed. Thus, it reduces the classification accuracy. For resolving these issues, this work proposes a hybrid optimization algorithm that combines the grasshopper optimization algorithm and the crow search algorithm for feature selection and classification of the breast mass with multilayer perceptron. The simulation is experimented with using MATLAB 2019a. The efficacy of the proposed hybrid grasshopper optimization‐crow search algorithm with multilayer perceptron system is compared to multilayer perceptron based algorithms of enhanced and adaptive genetic algorithm, teaching learning‐based whale optimization algorithm, butterfly optimization algorithm, whale optimization algorithm, and grasshopper optimization algorithm. From the results obtained, the proposed grasshopper optimization‐crow search algorithm with the multilayer perceptron method outperforms the comparative models in terms of classification accuracy (97.1%), sensitivity (98%), and specificity (95.4%) for the mammographic image analysis society dataset.

Blockchain technology support of Circular Economy (CE) concepts offers a promising route to sustainable development in the fields of business, education, healthcare, and the Internet of Things. The integrity of miners is essential to maintaining system reliability in public blockchains. This integrity is threatened by malicious activities like selfish mining and block withholding (BWH). Thus, accurately forecasting honest miners has become a crucial research issue. Although miner behaviour can be modelled by machine learning (ML) classifiers, their effectiveness is heavily reliant on the quality of the input characteristics. Optimal feature selection remains a major challenge in this context. This study addresses this issue using Genetic Algorithms (GA) and Differential Evolution (DE), both proven for optimization tasks. However, conventional GA and DE often suffer from slow convergence and suboptimal solutions. To overcome these limitations, we propose Knowledge-based GA (KBGA) and Knowledge-based DE (KBDE) by embedding domain-specific knowledge. These enhanced methods increase the viability of feature selection solutions and search efficiency. Comprehensive analyses show that KBGA and KBDE predict honest miner behaviour better than conventional GA and DE. The superiority of the suggested methods is further confirmed by statistical studies. This work promotes CE-driven sustainable development endeavours and advances secure Blockchain platforms.

In this study, we propose a Binary Particle Swarm Optimization algorithm hybridizing with Oppositionbased Learning for solving the feature selection problem. Opposition-based Learning is used in three different ways: (1) opposition-based population initialization; (2) opposition-based generation jumping; and (3) opposition-based population initialization and generation jumping. We conduct experiments on two medicine data sets. Based on the results, the oppositionbased population initialization and generation jumping performs better. Additionally, we investigate the effect of the eight different transfer functions on the performance of the proposed approach. Among the eight transfer functions, the sigmoid function (S1(x)) yields better performance than others. To evaluate the performance of the proposed method, the Binary Particle Swarm Optimization algorithm is applied to the problem. The results reveal that our approach outperforms the other methods.

Artificial intelligence (AI) and machine learning (ML) prediction can change education in a drastic way, where there can be both improvements and regressions concerning the way learning is approached. With individualized learning experiences, being able to spot the students who are falling behind, and customizing the course materials and tests that are fully customized, educators will help students achieve their individualized needs. We at Grand Canyon University conducted our study among 250 students in order to find out how they are interacting with AI in their academic journey. Using the binary Particle Swarm Optimization - Whale Optimization Algorithm for feature selection and the predictive modeling Linear Regression, we came up with vivid findings. For example, the bPSO-Guided WOA algorithm was characterized by a typical Average error of 0.25934, signifying the feat it was in feature selection, and the Linear Regression model particularly stood out in its sustainably low mean squared error (MSE), with a really admirable result of 1.39069E-31. Such evidence indicates the remarkable ability of AI and ML methods to develop true and relevant forecasts by providing teachers with possible and efficient decisions, thus improving the standards and effectiveness of education.

Cognitive load detection using circulant singular spectrum analysis and Binary Harris Hawks Optimization based feature selection