Binary Dragonfly Research Articles

Coronavirus herd immunity optimizer with greedy crossover for feature selection in medical diagnosis

The importance of medical data and the crucial nature of the decisions that are based on such data, as well as the large increase in its volume, has encouraged researchers to develop feature selection (FS)-based approaches to identify the most relevant data for specific medical problems In this paper, two intelligent wrapper FS approaches based on a new metaheuristic algorithm named the coronavirus herd immunity optimizer (CHIO) were applied with and without the incorporation of a greedy crossover (GC) operator strategy to enhance exploration of the search space by CHIO. The two proposed approaches, CHIO and CHIO-GC, were evaluated using 23 medical benchmark datasets and a real-world COVID-19 dataset. The experimental results indicated that CHIO-GC outperformed CHIO in terms of search capability, as reflected in classification accuracy, selection size, F-measure, standard deviation and convergence speed. The GC operator was able to enhance the balance between exploration and exploitation of the CHIO in the search and correct suboptimal solutions for faster convergence. The proposed CHIO-GC was also compared with two previous wrapper FS approaches, namely, binary moth flame optimization with Lévy flight (LBMFO_V3) and the hyper learning binary dragonfly algorithm (HLBDA), as well as four filter methods namely, Chi-square, Relief, correlation-based feature selection and information gain. CHIO-GC surpassed LBMFO_V3 and the four filter methods with an accuracy rate of 0.79 on 23 medical benchmark datasets. CHIO-GC also surpassed HLBDA with an accuracy rate of 0.93 when applied to the COVID-19 dataset. These encouraging results were obtained by striking a sufficient balance between the two search phases of CHIO-GC during the hunt for correct solutions, which also increased the convergence rate. This was accomplished by integrating a greedy crossover technique into the CHIO algorithm to remedy the inferior solutions found during premature convergence and while locked into a local optimum search space.

Hybrid Binary Dragonfly Algorithm with Simulated Annealing for Feature Selection.

There are various fields are affected by the growth of data dimensionality. The major problems which are resulted from high dimensionality of data including high memory requirements, high computational cost, and low machine learning classifier performance. Therefore, proper selection of relevant features from the set of available features and the removal of irrelevant features will solve these problems. Therefore, to solve the feature selection problem, an improved version of Dragonfly Algorithm (DA) is proposed by combining it with Simulated Annealing (SA), where the improved algorithm named BDA-SA. To solve the local optima problem of DA and enhance its ability in selecting the best subset of features for classification problems, Simulated Annealing (SA) was applied to the best solution found by Binary Dragonfly algorithm in attempt to improve its accuracy. A set of frequently used data sets from UCI repository was utilized to evaluate the performance of the proposed FS approach. Results show that the proposed hybrid approach, named BDA-SA, has superior performance when compared to wrapper-based FS methods including a feature selection method based on the basic version of Binary Dragonfly Algorithm.Supplementary InformationThe online version contains supplementary material available at 10.1007/s42979-021-00687-5.

Gene selection using hybrid dragonfly black hole algorithm: A case study on RNA-seq COVID-19 data

This paper introduces a new hybrid approach (DBH) for solving gene selection problem that incorporates the strengths of two existing metaheuristics: binary dragonfly algorithm (BDF) and binary black hole algorithm (BBHA). This hybridization aims to identify a limited and stable set of discriminative genes without sacrificing classification accuracy, whereas most current methods have encountered challenges in extracting disease-related information from a vast amount of redundant genes. The proposed approach first applies the minimum redundancy maximum relevancy (MRMR) filter method to reduce the dimensionality of feature space and then utilizes the suggested hybrid DBH algorithm to determine a smaller set of significant genes. The proposed approach was evaluated on eight benchmark gene expression datasets, and then, was compared against the latest state-of-art techniques to demonstrate algorithm efficiency. The comparative study shows that the proposed approach achieves a significant improvement as compared with existing methods in terms of classification accuracy and the number of selected genes. Moreover, the performance of the suggested method was examined on real RNA-Seq coronavirus-related gene expression data of asthmatic patients for selecting the most significant genes in order to improve the discriminative accuracy of angiotensin-converting enzyme 2 (ACE2). ACE2, as a coronavirus receptor, is a biomarker that helps to classify infected patients from uninfected in order to identify subgroups at risk for COVID-19. The result denotes that the suggested MRMR-DBH approach represents a very promising framework for finding a new combination of most discriminative genes with high classification accuracy.

S-shaped and V-shaped gaining-sharing knowledge-based algorithm for feature selection

In machine learning, searching for the optimal feature subset from the original datasets is a very challenging and prominent task. The metaheuristic algorithms are used in finding out the relevant, important features, that enhance the classification accuracy and save the resource time. Most of the algorithms have shown excellent performance in solving feature selection problems. A recently developed metaheuristic algorithm, gaining-sharing knowledge-based optimization algorithm (GSK), is considered for finding out the optimal feature subset. GSK algorithm was proposed over continuous search space; therefore, a total of eight S-shaped and V-shaped transfer functions are employed to solve the problems into binary search space. Additionally, a population reduction scheme is also employed with the transfer functions to enhance the performance of proposed approaches. It explores the search space efficiently and deletes the worst solutions from the search space, due to the updation of population size in every iteration. The proposed approaches are tested over twenty-one benchmark datasets from UCI repository. The obtained results are compared with state-of-the-art metaheuristic algorithms including binary differential evolution algorithm, binary particle swarm optimization, binary bat algorithm, binary grey wolf optimizer, binary ant lion optimizer, binary dragonfly algorithm, binary salp swarm algorithm. Among eight transfer functions, V4 transfer function with population reduction on binary GSK algorithm outperforms other optimizers in terms of accuracy, fitness values and the minimal number of features. To investigate the results statistically, two non-parametric statistical tests are conducted that concludes the superiority of the proposed approach.

Driving behavior oriented torque demand regulation for electric vehicles with single pedal driving

Driving behaviors, induced by psychological activities and environment stimulation, impose the dominant impact on vehicle driving performance. To exhaustively improve the performance of electric vehicles (EVs), information unscrambled from various driving behaviors is recommended to be incorporated into the controlling process. In this context, a novel method is presented to regulate the torque demand of EVs with single pedal driving (SPD) by efficiently interpreting intention from different driving behaviors for eco driving. Specifically, a brand-new driving behavior identifier (DBI) is constructed by integrally employing the binary dragonfly algorithm (BDA) and adaptive neuro-fuzzy inference system with particle swarm optimization (ANFIS-PSO). Simultaneously, the whale optimization algorithm (WOA) generates the torque demand look-up tables (TDLTs) offline under different driving behaviors for SPD by referring to the constraints from drivability and energy efficiency. In the instant implementation, the driving behaviors are identified instantaneously by the DBI, and the homologous TDLTs are assigned to vehicle controller, thereby attaining efficient control of vehicle powertrain. A case study about the vehicle traction control is performed to validate the prospective optimal performance of the proposed method and further evaluate the impact on vehicle performance from driving behaviors.

An enhanced binary dragonfly algorithm based on a V-shaped transfer function for optimization of pump scheduling program in water supply systems (case study of Iran)

With the continual growth of population and shortage of energy resources, the optimal consumption of these resources is of particular importance. One of these energy sources is electricity, with a significant amount being used in pumping stations for water distribution systems (WDS). Determining the proper pumping schedule can make significant savings in energy consumption and particularly in costs. This study aims to present an improved population-based nature-inspired optimization algorithmfor pumping scheduling program in WDS. To address this issue, the binary dragonfly algorithm based on a new transfer-function coupled with the EPANET hydraulic simulation model is developed to reduce the energy consumption of pumping stations. The proposed model was firstly implemented and evaluated on a benchmark test example, then on a real water pumping station. Comparison of the proposed method and the genetic algorithm (GA), evolutionary algorithm (EA), ant colony optimization (ACO), artificial bee colony (ABC), particle swarm optimization (PSO), and firefly (FF) was conducted on the benchmark test example, while the obtained results indicate that the proposed framework is more computationally efficient and reliable. The results of the real case study show that while considering all different constraints of the problem, the proposed model can decrease the cost of energy up to 27% in comparison with the current state of operation.

A hybrid artificial neural network classifier based on feature selection using binary dragonfly optimization for breast cancer detection

Medical image analysis has become a challenging task as it contributes to disease diagnosis. Breast cancer has been the prominent reason for death among women. While analysing mammogram images, there is a need for clear differentiation of between benign and malignant tissues. Also, early detection of breast masses lead to prediction of breast cancer at the initial stage and minimizes risk of death. In this work, the image is preprocessed using Median filter and is segmented using Fuzzy C Means clustering. Fuzzy C-Means clustering algorithm helps in extracting the region of interest by allocating pixels with similar characteristics into a single group. A pixel may be present in various clusters with different membership values. The belongingness of a pixel to a cluster is decided by the highest membership value. Then the statistical, texture and shape features are extracted from the image. Since there may be many features that are less relevant for classification process, prominent features are selected with the help of Binary Dragonfly Optimization Algorithm and the selected features are fed into a Feed Forward Neural Network trained with Back Propagation Learning to classify the mass as benign or malignant. Experiments are conducted over 320 images from mini-MIAS database out of which 200 ROIs are used in training and 120 ROIs are used in testing phase. The region of interest from given mammogram images are extracted successfully and classified with an accuracy of 98.75%.

Binary Spring Search Algorithm for Solving Various Optimization Problems

One of the most powerful tools for solving optimization problems is optimization algorithms (inspired by nature) based on populations. These algorithms provide a solution to a problem by randomly searching in the search space. The design’s central idea is derived from various natural phenomena, the behavior and living conditions of living organisms, laws of physics, etc. A new population-based optimization algorithm called the Binary Spring Search Algorithm (BSSA) is introduced to solve optimization problems. BSSA is an algorithm based on a simulation of the famous Hooke’s law (physics) for the traditional weights and springs system. In this proposal, the population comprises weights that are connected by unique springs. The mathematical modeling of the proposed algorithm is presented to be used to achieve solutions to optimization problems. The results were thoroughly validated in different unimodal and multimodal functions; additionally, the BSSA was compared with high-performance algorithms: binary grasshopper optimization algorithm, binary dragonfly algorithm, binary bat algorithm, binary gravitational search algorithm, binary particle swarm optimization, and binary genetic algorithm. The results show the superiority of the BSSA. The results of the Friedman test corroborate that the BSSA is more competitive.

Deep Learning Hybrid with Binary Dragonfly Feature Selection for the Wisconsin Breast Cancer Dataset

Breast cancer is the world’s top cancer affecting women. While the danger of the factors varies from a place, lifestyle, and diet. Treatment procedures after discovering a confirmed cancer case can reduce the risk of the disease. Unfortunately, breast cancers that arise in low and middle-income countries are diagnosed at a very late stage in which the chances of survival are impeded and reduced. Early detection is therefore required not only to improve the accuracy of discovering breast cancer but also to increase the chances of making the right decision on a successful treatment plan. There have been several studies tending to build software models utilizing machine learning and soft computing techniques for cancer detection. This research aims to build a model scheme to facilitate the detection of breast cancer and to provide the exact diagnosis. Improving the accuracy of a proposed model has, therefore, been one of the key fields of study. The model is based on deep learning that intends to develop a framework to accurately separate benign and malignant breast tumors. This study optimizes the learning algorithm by applying the Dragonfly algorithm to select the best features and perfect parameter values of the deep learning model. Moreover, it compares deep learning results against that of support vector machine (SVM), random forest (RF), and k nearest neighbor (KNN). Those classifiers are chosen as they are the most reliable algorithms having a solid fingerprint in the field of clinical data classification. Consequently, the hybrid model of deep learning combined with binary dragonfly has accurately classified between benign and malignant breast tumors with fewer features. Besides, deep learning model has achieved better accuracy in classifying Wisconsin Breast Cancer Database using all available features.

Feature Selection with Binary Symbiotic Organisms Search Algorithm for Email Spam Detection

One method to increase classifier accuracy is using Feature Selection (FS). The main idea in the FS is reducing complexity, eliminating irrelevant information, and deleting a subset of input features that either have little information or have no information for prediction. In this paper, three efficient binary methods based on the Symbiotic Organisms Search (SOS) algorithm were presented for solving the FS problem. In the first and second methods, several S_shaped and V_shaped transfer functions were used for the binarization of the SOS, respectively. These methods were called BSOSS and BSOSV. In the third method, two new operators called Binary Mutualism Phase (BMP) and Binary Commensalism Phase (BCP) were presented for binarization of the SOS, named Efficient Binary SOS (EBSOS). The proposed methods were run on 18 standard UCI datasets and compared to the base and important meta-heuristic algorithms. The test results showed that the EBSOS method has the best performance among the three proposed methods for the binarization of the SOS. Finally, the EBSOS method was compared to the Genetic Algorithm (GA), Binary Bat Algorithm (BBA), Binary Particle Swarm Optimization (BPSO) Algorithm, Binary Flower Pollination Algorithm (BFPA), Binary Grey Wolf Optimizer (BGWO) Algorithm, Binary Dragonfly Algorithm (BDA), and Binary Chaotic Crow Search Algorithm (BCCSA). In addition, the EBSOS method was executed on the spam email dataset with the KNN, NB, SVM, and MLP classifiers. The results showed that the EBSOS method has better performance compared to other methods in terms of feature count and accuracy criteria. Furthermore, it was practically evaluated on spam email detection in particular.

An enhanced color visual cryptography scheme based on the binary dragonfly algorithm

ABSTRACT Visual cryptography is an encryption technique that decomposes a secret image into multiple shares that are digitally ‘overlapped’ based on logical operations to recover the original secret image. Each individual share cannot reveal clues about the secret image. Existing visual cryptography schemes suffer from problems such as pixel expansion, high computational complexity and poor decryption quality. To overcome these problems, we propose a new 2-out-of-2 secret sharing scheme for color images based on the binary dragonfly algorithm. The dragonfly algorithm is used to determine optimal color levels for the encryption process, which leads to reconstructed images with the higher quality upon decryption with minimal effect on the overall computational complexity. The proposed scheme is also non-expansible, whereby the share images are of the same size as the original secret image. This leads to reduced memory requirements as well as improved image quality. We analyze the strength of the proposed scheme based on a standard set of metrics which includes an evaluation of its resistance against cryptanalytic attacks, histogram, correlation, entropy and encryption quality. Results show that the proposed visual cryptography scheme has desired properties such as improved recovered image quality, near-ideal statistical characteristics and fast encryption speed, depicting an improvement over existing schemes.

Hybrid Binary Dragonfly Optimization Algorithm with Statistical Dependence for Feature Selection

The aim of the feature selection technique is to obtain the most important information from a specific set of datasets. Further elaborations in the feature selection technique will positively affect the classification process, which can be applied in various areas such as machine learning, pattern recognition, and signal processing. In this study, a hybrid algorithm between the binary dragonfly algorithm (BDA) and the statistical dependence (SD) is presented, whereby the feature selection method in discrete space is modeled as a binary-based optimization algorithm, guiding BDA and using the accuracy of the k-nearest neighbors classifier on the dataset to verify it in the chosen fitness function. The experimental results demonstrated that the proposed algorithm, which we refer to as SD-BDA, outperforms other algorithms in terms of the accuracy of the results represented by the cost of the calculations and the accuracy of the classification.

A Hyper Learning Binary Dragonfly Algorithm for Feature Selection: A COVID-19 Case Study

The rapid expansion of information science has caused the issue of “the curse of dimensionality”, which will negatively affect the performance of the machine learning model. Feature selection is typically considered as a pre-processing mechanism to find an optimal subset of features from a given set of all features in the data mining process. In this article, a novel Hyper Learning Binary Dragonfly Algorithm (HLBDA) is proposed as a wrapper-based method to find an optimal subset of features for a given classification problem. HLBDA is an enhanced version of the Binary Dragonfly Algorithm (BDA) in which a hyper learning strategy is used to assist the algorithm to escape local optima and improve searching behavior. The proposed HLBDA is compared with eight algorithms in the literature. Several assessment indicators are employed to evaluate and compare the effectiveness of these methods over twenty-one datasets from the University of California Irvine (UCI) repository and Arizona State University. Also, the proposed method is applied to a coronavirus disease (COVID-19) dataset. The results demonstrate the superiority of HLBDA in increasing classification accuracy and reducing the number of selected features.22The source code of HLBDA is publicly available at https://seyedalimirjalili.com/da.

A novel binary gaining–sharing knowledge-based optimization algorithm for feature selection

To obtain the optimal set of features in feature selection problems is the most challenging and prominent problem in machine learning. Very few human-related metaheuristic algorithms were developed and solved this type of problem. It motivated us to check the performance of recently developed gaining–sharing knowledge-based optimization algorithm (GSK), which is based on the concept of gaining and sharing knowledge of humans throughout their lifespan. It depends on two stages: beginners–intermediate gaining and sharing stage and intermediate–experts gaining and sharing stage. In this study, two approaches are proposed to solve feature selection problems: FS-BGSK: a novel binary version of GSK algorithm that relies on these two stages with knowledge factor 1 and FS-pBGSK: a population reduction technique that is employed on BGSK algorithm to enhance the exploration and exploitation quality of FS-BGSK. The proposed approaches are checked on twenty two feature selection benchmark datasets from UCI repository that contains small, medium and large dimensions datasets. The obtained results are compared with seven state-of-the-art metaheuristic algorithms; binary differential evolution, binary particle swarm optimization algorithm, binary bat algorithm, binary grey wolf optimizer, binary ant lion optimizer, binary dragonfly algorithm and binary salp swarm algorithm. It concludes that FS-pBGSK and FS-BGSK outperform other algorithms in terms of accuracy, convergence and robustness in most of the datasets.

Binary Whale Optimization Algorithm for Dimensionality Reduction

Feature selection (FS) was regarded as a global combinatorial optimization problem. FS is used to simplify and enhance the quality of high-dimensional datasets by selecting prominent features and removing irrelevant and redundant data to provide good classification results. FS aims to reduce the dimensionality and improve the classification accuracy that is generally utilized with great importance in different fields such as pattern classification, data analysis, and data mining applications. The main problem is to find the best subset that contains the representative information of all the data. In order to overcome this problem, two binary variants of the whale optimization algorithm (WOA) are proposed, called bWOA-S and bWOA-V. They are used to decrease the complexity and increase the performance of a system by selecting significant features for classification purposes. The first bWOA-S version uses the Sigmoid transfer function to convert WOA values to binary ones, whereas the second bWOA-V version uses a hyperbolic tangent transfer function. Furthermore, the two binary variants introduced here were compared with three famous and well-known optimization algorithms in this domain, such as Particle Swarm Optimizer (PSO), three variants of binary ant lion (bALO1, bALO2, and bALO3), binary Dragonfly Algorithm (bDA) as well as the original WOA, over 24 benchmark datasets from the UCI repository. Eventually, a non-parametric test called Wilcoxon’s rank-sum was carried out at 5% significance to prove the powerfulness and effectiveness of the two proposed algorithms when compared with other algorithms statistically. The qualitative and quantitative results showed that the two introduced variants in the FS domain are able to minimize the selected feature number as well as maximize the accuracy of the classification within an appropriate time.

Multifactor authentication system based on color visual cryptography, facial recognition, and dragonfly optimization

ABSTRACT Facial recognition as an authentication factor requires that facial images and features are tamper-free. Visual cryptography (VC) is commonly used for this purpose but leads to additional computational overhead and lower recognition accuracy. To address these issues, we propose a multifactor authentication (MFA) system based on facial recognition that uses VC to secure biometric data, and also as a second authentication factor. The shares generated by VC are used as authentication tokens. These tokens are verified by the same facial recognition algorithm used to recognize a live facial image of the user. This simple albeit novel approach leads to lower computational cost because the same algorithm can be used to verify both authentication factors. To maximize verification accuracy, the binary dragonfly optimization algorithm is used to maximize the quality of the recovered image from VC, as well as the accuracy of the facial recognition algorithm itself through feature selection. The combination of these separate ideas leads to a novel MFA system that is efficient and highly accurate. Experimental verification based on various face image databases depicts the proposed system’s security, efficiency, and near-ideal recognition accuracy of up to 99.81%.

Optimal Planning for the Development of Power System in Respect to Distributed Generations Based on the Binary Dragonfly Algorithm

With the increasing number of population and the rising demand for electricity, providing safe and secure energy to consumers is getting more and more important. Adding dispersed products to the distribution network is one of the key factors in achieving this goal. However, factors such as the amount of investment and the return on the investment on one side, and the power grid conditions, such as loss rates, voltage profiles, reliability, and maintenance costs, on the other hand, make it more vital to provide optimal annual planning methods concerning network development. Accordingly, in this paper, a multilevel method is presented for optimal network power expansion planning based on the binary dragonfly optimization algorithm, taking into account the distributed generation. The proposed objective function involves the minimization of the cost of investment, operation, repair, and the cost of reliability for the development of the network. The effectiveness of the proposed model to solve the multiyear network expansion planning problem is illustrated by applying them on the 33-bus distribution network and comparing the acquired results with the results of other solution methods such as GA, PSO, and TS.

A binary water wave optimization for feature selection

A search method that finds a minimal subset of features (over a feature space) that yields maximum classification accuracy is proposed. This method employs rough set theory (RST) along with a newly introduced binary version of the water wave optimization approach (WWO) which is denoted by BWWO. WWO simulates the phenomena of water waves, such as propagation, refraction, and breaking and is one of the newest nature inspired methods for global optimization problems. In our approach, BWWO utilizes the phenomena of water waves propagation, refraction, and breaking in a binary version. Two main experiments based on the rough set approach and wrapper method as a part of the objective function are carried out to verify the performance of the proposed algorithm. In the first experiment, the effectiveness of the proposed approach based on RST is demonstrated on 16 different datasets. The proposed approach is compared with various typical attribute reduction methods and popular optimizers in the literature, such as ant colony, nonlinear great deluge algorithm, scatter search and others. For the second experiment, a feature subset that maximizes the classification accuracy (using cross-validated kNN classifier) with minimizing the number of selected features is obtained over 17 different datasets. In wrapper experiment BWWO is compared with the binary gray wolf optimization, binary particle swarm optimizer, binary cat swarm optimization, binary dragonfly algorithm and the binary bat algorithm. The computational results demonstrate the efficiency and effectiveness of the proposed approach in finding a minimal features subset that maximize the classification accuracy. Furthermore, Friedman test and Wilcoxon's rank-sum test are carried out at 5% significance level in this study.

IBDA: Improved Binary Dragonfly Algorithm With Evolutionary Population Dynamics and Adaptive Crossover for Feature Selection

Feature selection is an effective method to eliminate irrelevant, redundant and noisy features, which improves the performance of classification and reduces the computational burden in machine learning. In this paper, an improved binary dragonfly algorithm (IBDA) which extends from the conventional dragonfly algorithm (DA) is proposed as a search strategy to design a wrapper-based feature selection method. First, a novel evolutionary population dynamics (EPD) strategy is introduced in IBDA to enhance the exploitation ability while ensuring population diversity of the algorithm. Second, IBDA proposes a novel crossover operator which establishes connections between the crossover rates and iterations so that making the algorithm can adjust the crossover rates of solutions dynamically, thereby balancing the exploitation and exploration of the algorithm. Finally, a binary mechanism is proposed to make the algorithm suitable for the binary feature selection problems. Simulations are conducted on 27 classical datasets from the UC Irvine Machine Learning Repository, and the results demonstrate that the proposed IBDA has better performance than some other comparison algorithms. Moreover, the effectiveness and performance of the proposed improved factors are evaluated by tests.

Robust Frequency Control of an Islanded AC Micro Grid using BDA Optimized 3DOF Controller under Plug in Electric Vehicle

The article presents the effectiveness of a Binary Dragonfly Algorithm (BDA) based 3-DOF controller for robust frequency control in an islanded AC micro-grid system under different uncertainties. A micro-grid is incorporated with the integration of various renewable energy based distributed generations (DG). The proposed micro-grid system is structured with wind turbine generator (WTG), Photo voltaic (PV) system, Diesel engine generator (DEG), Micro-turbines (MT), Aqua electrolyzer based Fuel Cells(FC) and with few energy storage devices i.e Battery energy storage (BES) and Flywheel energy storage (FES). Moreover a chargeable plug in electric vehicle is effected as load side demand while obtaining frequency control mechanism in micro-grid system. However large dynamics, low inertia and incurred uncertainties of most DG system affects system performance especially on system frequency seriously. In view of this to obtain robust control mechanism in islanded micro-grid system the article proposes a novel Binary Dragonfly Algorithm based 3-DOF controller to ensure servicing of good quality power to remote consumers. The performances of proposed BDA optimized 3-DOF controller is compared with conventional PSO, GA technique based PID and PI controller in order to justify supremacy of proposed approaches. Finally it has been suggested that the proposed BDA optimized 3-DOF controller is more effectiveness over other optimized controllers.