In this paper, we propose a multivariate fast iterative filtering (MvFIF) decomposition algorithm, entropy-based features, and a nature-inspired feature selection approach for Alzheimer’s disease (AD) detection using electroencephalogram (EEG) signals. Where, the MvFIF decomposes the multichannel EEG signals into multichannel intrinsic mode functions (MIMFs). The entropy features: dispersion entropy (DispEn) and distribution entropy (DistEn) are extracted from the MIMFs. Afterward, five nature-inspired feature selection algorithms are applied to reduce the feature space by selecting the relevant features for the AD. The selected features are finally used for the binary classification to distinguish AD patients from healthy control (HC) subjects using different classifiers. In addition, lobe-wise analysis is performed to understand the neural activity, diagnose AD, and guide targeted treatments. We show that the binary differential evolution optimization (BDEO) feature selection method with the support vector machine (SVM) classifier achieves the highest accuracy of 90.78% with standard deviation (SD) of 1.96% using 10-fold cross-validation (CV) and 75% with SD of 18.20% using leave-one-subject-out CV (LOSO-CV). In lobe-wise analysis, XGBoost classifier with temporal lobe gives the highest accuracy of 80.06% with SD of 1.53% using 10-fold CV and 70.78% with SD of 23.93% using LOSO-CV. The proposed approach surpasses the current leading techniques in AD detection utilizing EEG signals.

Association rule mining is a fundamental data mining technique for uncovering latent relationships among variables in large-scale datasets. However, conventional approaches rely on single-metric filtering strategies, which are insufficient for capturing the inherent multi-criteria nature of rule quality. To address this limitation, this study formulates ARM as a many-objective optimization problem and proposes a hybrid algorithm, NSGA-II/DE-ARM, that simultaneously optimizes four rule-quality measures: support, confidence, lift, and NetConf. The proposed algorithm enhances the NSGA-II framework by integrating binary differential evolution operators, an adaptive operator selection mechanism, lift-weighted tournament selection, and a constraint-domination principle combined with a dynamic minimum support threshold. Its performance was evaluated using two datasets: a SIPRI-World Bank panel dataset consisting of defense industry and macroeconomic indicators covering 46 items over the 2002-2023 period, and the UCI Mushroom benchmark dataset consisting of 118 items. Across 30 independent runs on the SIPRI-World Bank dataset, NSGA-II/DE-ARM outperformed the Apriori baseline in all four metrics (mean lift = 4.748, confidence = 0.853, support = 0.146, NetConf = 0.789), with large effect sizes (Cohen's d = 1.77-5.77, p < 0.001 in each case). On the Mushroom benchmark dataset, the proposed method also achieved substantial improvements, with Cohen's d values ranging from 0.93 to 6.16. NSGA-II/DE-ARM generated 68 Pareto-optimal rules in a representative run and achieved the highest hypervolume values on both datasets, with HV = 3.231 for SIPRI-World Bank and HV = 6.262 for Mushroom. These results suggest that NSGA-II/DE-ARM offers decision-makers a broader and more balanced multi-criteria solution set than single-metric filtering approaches.

A Novel Personal Best- Control Binary Particle Swarm Optimization (NPbest-BPSO) based electromyography (EMG) signal feature selection and classification

DNA microarray technology produces high-dimensional gene expression data, where many genes are irrelevant to disease. Effective feature selection is thus essential to mitigate the curse of dimensionality and enhance classification performance. This study introduces a multi-objective feature selection approach employing a Clustering-Based Binary Differential Evolution (CBDE) mutation to identify a compact set of disease-relevant genes. The proposed DeFs-CBDE algorithm was assessed on four gene expression datasets, i.e. brain, breast, lung, and central nervos system cancer by selecting informative feature subsets and evaluating them using five state-of-the-art classifiers, i.e., Support Vector Machine (SVM), Naive Bayes, K-Nearest Neighbors, Decision Tree (DT), and Random Forest. The DeFs-CBDE method achieved of 100 % accuracy on the brain dataset with three classifiers. On the lung dataset, DeFs-CBDE reached 97.56 % accuracy with SVM and DT. For the breast dataset, DeFs-CBDE attained 93.33 % accuracy very close to the highest score of 93.81 % accuracy. The CNS dataset proved the most challenging, where it achieved 91.67 % accuracy with SVM. Across all datasets, DeFs-CBDE consistently achieved high classification performance.

For increasingly complex real-world optimization problems, a large number of researches have shown that differential evolution (DE) is an effective problem-solving algorithm. Despite its proven efficacy, DE still encounters challenges associated with rapid convergence and vulnerability to local optima. We introduce a novel methodology, namely binary tree population structure-based differential evolution (BTDE), to mitigate these issues. BTDE introduces a binary tree population structure comprising multiple layers of populations, strategically combined to exploit valuable information across diverse populations and ensure population diversity during the evolutionary process. To evaluate the performance of BTDE, we conduct rigorous assessments utilizing two benchmark test sets, including the CEC2017 benchmark functions and the CEC2011 real-world problems. Furthermore, we investigate the sensitivity of BTDE's parameters and conduct ablation studies to examine the individual layer's influence. The experimental results demonstrate that BTDE outperforms state-of-the-art DE variants, CEC competition winners, and selected powerful variants of the classical algorithm, substantiating its superiority in tackling complex problems.

Evolutionary Algorithms (EAs) are those based on the phenomenon of survival of the fittest. Differential Evolution (DE) is a member of this family, and it is well-suited for handling problems with real-valued variables. However, to use DE to solve binary problems, it is necessary to employ some adaptation. The primary objective of the present study is to develop a new binary version of DE. The proposed algorithm is called Binary Differential Evolution with a limited maximum number of dimension changes (NBDE), and it was tested with the OneMax Problem, five variants of the Knapsack Problem (KP), and Feature Selection (FS). The results showed that NBDE is competitive and superior to the other tested algorithms in many instances. For the 0/1 KP and 0/1 Multidimensional KP, NBDE outperforms all the other algorithms for all instances. For the FS problem, the proposed algorithm demonstrates good accuracy as its primary quality. The proposed algorithm exhibits a satisfactory performance, particularly in high-dimensional problems, which appears to be a quality inherited from the method that inspired its creation. This is particularly interesting because it provides empirical evidence that the importation of operators can perpetuate a pattern of behavior in algorithms with different structures.

This paper introduces a novel framework for designing wideband antenna arrays using self-similar Eisenstein fractal geometries combined with multi-objective evolutionary optimization techniques. The approach employs multi-objective binary differential evolution (MO-BDE) for array thinning and multi-objective particle swarm optimization (MO-PSO) for optimizing amplitude excitations. This integrated methodology reduces the number of active elements while enhancing overall array performance. The optimization process targets minimizing peak side lobe levels and maximizing directivity over a broad frequency range. Two designs are explored: one optimized at a primary frequency, and another providing consistent wideband behavior. The proposed method achieves a 37.5% reduction in active elements. Design A shows an SLL reduction of −12 dB at the target frequency, while Design B maintains up to −3 dB SLL improvement across the bandwidth. The results confirm the efficacy of the proposed synthesis method for developing scalable, energy-efficient antenna arrays for next-generation systems.

Abstract Advancements in radiation-based imaging and computational intelligence have significantly improved medical diagnostics, particularly in dermatology. This study presents an ensemble-based skin lesion classification framework that integrates deep neural networks (DNNs) with transfer learning, a customized DNN, and an optimized self-learning binary differential evolution (SLBDE) algorithm for feature selection and fusion. Leveraging computational techniques alongside medical imaging modalities, the proposed framework extracts and fuses discriminative features from multiple pre-trained models to improve classification robustness. The methodology is evaluated on benchmark datasets, including ISIC 2017 and the Argentina Skin Lesion dataset, demonstrating superior accuracy, precision, and F1-score in melanoma detection. The proposed method achieved classification accuracy of 98.5% evaluated using LSVM classifier on the Argentina Skin Lesion dataset underscoring the robustness of the proposed methodology. The proposed approach provides a scalable and computational efficient solution for automated skin lesion classification- contributing to improved clinical decision-making and enhanced patient outcomes. By aligning artificial intelligence with radiation-based medical imaging and bioinformatics, this research advances dermatological computer-aided diagnosis (CAD) systems, minimizing misclassification rates and supporting early skin cancer detection. The proposed approach provides a scalable and computationally efficient solution for automated skin lesion analysis, contributing to improved clinical decision-making and enhanced patient outcomes.

Feature subset selection for big data via parallel chaotic binary differential evolution and feature-level elitism

It is a challenging task to select the informative features that can maintain the manifold structure in the original feature space. Many unsupervised feature selection methods still suffer the poor cluster performance in the selected feature subset. To tackle this problem, a feature subspace learning-based binary differential evolution algorithm is proposed for unsupervised feature selection. Firstly, a new unsupervised feature selection framework based on evolutionary computation is designed, in which the feature subspace learning and the population search mechanism are combined into a unified unsupervised feature selection. Secondly, a local manifold structure learning strategy and a sample pseudo-label learning strategy are presented to calculate the importance of the selected feature subspace. Thirdly, the binary differential evolution algorithm is developed to optimize the selected feature subspace, in which the binary information migration mutation operator and the adaptive crossover operator are designed to promote the searching for the global optimal feature subspace. Experimental results on various types of realworld datasets demonstrate that the proposed algorithm can obtain more informative feature subset and competitive cluster performance compared with eight state-of-the-art unsupervised feature selection methods.

Abstract The direct mating strategy, which is a constraint-handling method in multi-objective evolutionary algorithms, selects a second parent on the basis of the optimal direction in the objective space, disregarding feasibility. This approach, although effective in early generations, encounters difficulties as solutions better than the current Pareto solutions become scarce, reducing diversity and hindering exploration. Herein, we introduce a hybrid approach that combines direct mating with newly proposed local mating strategies, aiming to generate offspring around the Pareto optimal solutions to improve the solution search performance via a direct mating strategy. Our method addresses the limitations of conventional direct mating, thereby improving its applicability to real-world design problems. Our hybrid strategy employs local mating to select an additional parent close to the initially chosen parent, preserving solution diversity and effectively exploring the solution space. This enhances the effectiveness of direct mating across all generations. In addition, we introduce two types of crossover methods, i.e., simulated binary crossover and differential evolution, for parents selected by direct and local mating, respectively. The synergy among these techniques facilitates a robust search process that can efficiently yield superior solutions. We evaluated the proposed approach using three mathematical problems with distinct Pareto fronts and two practical applications, including an aerospace design problem. The performances of these methods were assessed on the basis of the averages and standard deviations of the hypervolume and inverted generational distance metrics across multiple solution attempts to determine the average performances and the dependencies on initial population variance. The obtained results demonstrate that the hybrid method significantly outperforms the existing methods in the case of constrained multi-objective optimization. Specifically, the hybrid method yielded higher average hypervolumes and lower dependency on initial population variance than the existing methods. The hybrid method also yielded a lower average and less dependency on initial population variance in the inverted generational distance compared to existing methods. This result suggests that the proposed method can obtain high-quality non-dominated solutions, regardless of the initial population.

Abstract The most common cause of mechanical failure is bearing failure, and the characteristics of each failure correspond to a certain degree of severity. This paper proposes a fault diagnosis model for detecting motor bearings. The model uses three steps: feature extraction, feature selection, and classification. In feature extraction, empirical mode decomposition, fast Fourier transform, and envelope analysis extract important features from the signals measuring the motor. In feature selection, a binary differential evolution and binary whale algorithm are developed and the storage space is increased to eliminate irrelevant features again. Finally, KNN and SVM are used to determine the stability of the bearing fault diagnosis model.

Increasing load and penetrating solar PhotoVoltaic (PV) systems tend higher in the future. Transmission Expansion Planning (TEP) is commonly used to solve this issue and is typically formed by using the peak load scenario, which is often assumed to represent the worst-case condition. Metaheuristic algorithms are predominantly used to address the TEP problem, which is frequently characterized by solutions becoming trapped in local optima. To address this challenge, this study proposes a TEP approach, utilizing an improved Binary Differential Evolution (BDE) algorithm in which the mutation factor and crossover rate are dynamically adjusted using a logistic map, enabling more effective exploration of the solution space. The simulation results are tested using the IEEE 14-bus system. The simulation results demonstrate that the improved BDE algorithm can decrease the likelihood of entrapment in local optima by providing a lower total cost by 15.89% when compared with the conventional BDE algorithm.

Image registration has demonstrated its significance as an essential tool for target recognition, classification, tracking, and damage assessment during natural catastrophes. The image registration process relies on the identification of numerous reliable features; thus, low resolutions, poor lighting conditions, and low image contrast substantially diminish the number of dependable features available for registration. Contrast stretching enhances image quality, facilitating the object detection process. In this study, we proposed a hybrid binary differential evolution and BAT optimization model to enhance contrast stretching by optimizing a decision variables in the transformation function. To validate its efficiency, the proposed approach is utilized as a preprocessor before feature extraction in image registration. Cross-comparison of detected features of enhanced images verses the original images during image registration validate the improvements in the image registration process.

ABSTRACTThe present study aims to identify damage in structures using seismic responses and a two‐stage method based on mixture of Gaussian (MOG) and Harris hawks optimization (HHO). Two‐dimensional (2‐D) frame structures under seismic loads are simulated using the finite element method (FEM). The placement of sensors is optimized using a combination of an iterated improved reduced system (IIRS) and the binary differential evolution (BDE) algorithm. The MOG classifier is trained to find the damaged story and damaged element type (i.e., whether it is a beam or a column) using the nodal acceleration responses at the optimal sensor placement of the seismically loaded structure. Thus, as the first step, the possibly damaged elements are located through the classifier. Then, in the second step, the damage is accurately located and quantified by HHO algorithm. The performance of the proposed method is assessed using the numerical results of 2‐D frames of 7 and 14 stories in different damage scenarios with and without considering noise. As a result, the efficiency of the proposed method for the seismic damage identification of 2‐D frames is revealed. Sensor positions are well optimized, and it causes the method to be highly effective. Moreover, MOG correctly finds a damaged story and identifies whether a damaged element is a beam or a column. Damage in the presence of noise is also localized and quantified precisely by HHO.

This research paper explores the performance of binary nature-inspired optimization algorithms as feature selection to enhance the identification of human activities using wearable technology. Utilization of nature-inspired algorithms for feature selection, as documented in scholarly literature, presents a promising opportunity to enhance machine learning and data analysis tasks, given their effectiveness in identifying relevant features, resulting in models with reduced computational complexity, improved predictive accuracy and easier interpretation. In the experiment, we conducted an evaluation of the effectiveness and efficiency of four nature-inspired binary algorithms for optimization namely Binary Particle Swarm Optimization (BPSO), Binary Grey Wolf Optimization algorithm (BGWO), Binary Differential Evolution algorithm (BDE), and Binary Salp Swarm algorithm (BSS) - in the context of human activity recognition (HAR). The outcomes of this comprehensive experimentation, conducted on two distinct human activity recognition (HAR) datasets, provide valuable insights. BPSO algorithm emerges as an adaptable and well-rounded performer, achieving a competitive balance between feature selection quality and computational efficiency in SBHAR dataset. Conversely, for the PAMAP2 dataset, BDE algorithm displays superior feature selection quality and BPSO algorithm maintains competitive performance and adaptability. In both datasets, the nature-inspired optimization algorithms have achieved remarkable feature reduction, demonstrating reductions of 48% and 50% respectively. The experiment results show how these algorithms could be used to improve methods for recognizing human activities using wearables technology, such as feature selection, parameter adjustment, and model optimization.

Binary metaheuristic algorithms for 0–1 knapsack problems: Performance analysis, hybrid variants, and real-world application

We propose a method for selecting the optimal set of weather features for wind energy prediction. This problem is tackled by developing a wrapper approach that employs binary differential evolution to search for the best feature subset, and an ensemble of artificial neural networks to predict the energy production from a wind plant. The main novelties of the approach are the use of features provided by different weather forecast providers and the use of an ensemble composed of a reduced number of models for the wrapper search. Its effectiveness is verified using weather and energy production data collected from a 34 MW real wind plant. The model is built using the selected optimal subset of weather features and allows for (i) a 1% reduction in the mean absolute error compared with a model that considers all available features and a 4.4% reduction compared with the model currently employed by the plant owners, and (ii) a reduction in the number of selected features by 85% and 50%, respectively. Reducing the number of features boosts the prediction accuracy. The implication of this finding is significant as it allows plant owners to create profitable offers in the energy market and efficiently manage their power unit commitment, maintenance scheduling, and energy storage optimization.

Accurate fault section diagnosis of power systems with a binary adaptive quadratic interpolation learning differential evolution

This manuscript presents commitment scheduling of power, heat and natural gas in presence of thermal generating units (TUs), cogeneration units (CUs), heat only units (HUs), solar PV plants, wind turbine generators and PEV parking lot with charging and discharging facility considering carbon capture. Power to gas technology comprising of carbon capture unit, electrolyzer, hydrogen storage unit, and methanation procedure, is used to provide natural gas demand using CO2 taken from TUs, CUs, HUs and hydrogen produced by electrolyzer. Total operative cost of TUs, CUs, HUs plus running cost of WGs and SPs are minimized whilst fulfilling a number of unit commitment constraints as well as generation scheduling constraints. Binary differential evolution along with priority list is applied for solving this problem. Numerical results of the system are comprehended for giving valued particulars for operative problem.