B-MFO: A Binary Moth-Flame Optimization for Feature Selection from Medical Datasets

Enhanced multi-layer perceptron for CO2 emission prediction with worst moth disrupted moth fly optimization (WMFO)

This study introduces the Worst Moth Disruption Strategy (WMFO) to enhance the Moth Fly Optimization (MFO) algorithm, specifically addressing challenges related to population stagnation and low diversity. The WMFO aims to prevent local trapping of moths, fostering improved global search capabilities. Demonstrating a remarkable efficiency of 66.6 %, WMFO outperforms the MFO on CEC15 benchmark test functions. The Friedman and Wilcoxon tests further confirm WMFO's superiority over state-of-the-art algorithms. Introducing a hybrid model, WMFO-MLP, combining WMFO with a Multi-Layer Perceptron (MLP), facilitates effective parameter tuning for carbon emission prediction, achieving an outstanding total accuracy of 97.8 %. Comparative analysis indicates that the MLP-WMFO model surpasses alternative techniques in precision, reliability, and efficiency. Feature importance analysis reveals that variables such as Oil Efficiency and Economic Growth significantly impact MLP-WMFO's predictive power, contributing up to 40 %. Additionally, Gas Efficiency, Renewable Energy, Financial Risk, and Political Risk explain 26.5 %, 13.6 %, 8 %, and 6.5 %, respectively. Finally, WMFO-MLP performance offers advancements in optimization and predictive modeling with practical applications in carbon emission prediction.

Feature Selection Problem and Metaheuristics: A Systematic Literature Review about Its Formulation, Evaluation and Applications

Feature selection is becoming a relevant problem within the field of machine learning. The feature selection problem focuses on the selection of the small, necessary, and sufficient subset of features that represent the general set of features, eliminating redundant and irrelevant information. Given the importance of the topic, in recent years there has been a boom in the study of the problem, generating a large number of related investigations. Given this, this work analyzes 161 articles published between 2019 and 2023 (20 April 2023), emphasizing the formulation of the problem and performance measures, and proposing classifications for the objective functions and evaluation metrics. Furthermore, an in-depth description and analysis of metaheuristics, benchmark datasets, and practical real-world applications are presented. Finally, in light of recent advances, this review paper provides future research opportunities.

A hybrid algorithm of grey wolf optimizer and harris hawks optimization for solving global optimization problems with improved convergence performance

The grey wolf optimizer is an effective and well-known meta-heuristic algorithm, but it also has the weaknesses of insufficient population diversity, falling into local optimal solutions easily, and unsatisfactory convergence speed. Therefore, we propose a hybrid grey wolf optimizer (HGWO), based mainly on the exploitation phase of the harris hawk optimization. It also includes population initialization with Latin hypercube sampling, a nonlinear convergence factor with local perturbations, some extended exploration strategies. In HGWO, the grey wolves can have harris hawks-like flight capabilities during position updates, which greatly expands the search range and improves global searchability. By incorporating a greedy algorithm, grey wolves will relocate only if the new location is superior to the current one. This paper assesses the performance of the hybrid grey wolf optimizer (HGWO) by comparing it with other heuristic algorithms and enhanced schemes of the grey wolf optimizer. The evaluation is conducted using 23 classical benchmark test functions and CEC2020. The experimental results reveal that the HGWO algorithm performs well in terms of its global exploration ability, local exploitation ability, convergence speed, and convergence accuracy. Additionally, the enhanced algorithm demonstrates considerable advantages in solving engineering problems, thus substantiating its effectiveness and applicability.

Enhancing Diagnosis of Rotating Elements in Roll-to-Roll Manufacturing Systems through Feature Selection Approach Considering Overlapping Data Density and Distance Analysis

Roll-to-roll manufacturing systems have been widely adopted for their cost-effectiveness, eco-friendliness, and mass-production capabilities, utilizing thin and flexible substrates. However, in these systems, defects in the rotating components such as the rollers and bearings can result in severe defects in the functional layers. Therefore, the development of an intelligent diagnostic model is crucial for effectively identifying these rotating component defects. In this study, a quantitative feature-selection method, feature partial density, to develop high-efficiency diagnostic models was proposed. The feature combinations extracted from the measured signals were evaluated based on the partial density, which is the density of the remaining data excluding the highest class in overlapping regions and the Mahalanobis distance by class to assess the classification performance of the models. The validity of the proposed algorithm was verified through the construction of ranked model groups and comparison with existing feature-selection methods. The high-ranking group selected by the algorithm outperformed the other groups in terms of training time, accuracy, and positive predictive value. Moreover, the top feature combination demonstrated superior performance across all indicators compared to existing methods.

A Systematic Review of the Whale Optimization Algorithm: Theoretical Foundation, Improvements, and Hybridizations

Despite the simplicity of the whale optimization algorithm (WOA) and its success in solving some optimization problems, it faces many issues. Thus, WOA has attracted scholars' attention, and researchers frequently prefer to employ and improve it to address real-world application optimization problems. As a result, many WOA variations have been developed, usually using two main approaches improvement and hybridization. However, no comprehensive study critically reviews and analyzes WOA and its variants to find effective techniques and algorithms and develop more successful variants. Therefore, in this paper, first, the WOA is critically analyzed, then the last 5 years' developments of WOA are systematically reviewed. To do this, a new adapted PRISMA methodology is introduced to select eligible papers, including three main stages: identification, evaluation, and reporting. The evaluation stage was improved using three screening steps and strict inclusion criteria to select a reasonable number of eligible papers. Ultimately, 59 improved WOA and 57 hybrid WOA variants published by reputable publishers, including Springer, Elsevier, and IEEE, were selected as eligible papers. Effective techniques for improving and successful algorithms for hybridizing eligible WOA variants are described. The eligible WOA are reviewed in continuous, binary, single-objective, and multi/many-objective categories. The distribution of eligible WOA variants regarding their publisher, journal, application, and authors' country was visualized. It is also concluded that most papers in this area lack a comprehensive comparison with previous WOA variants and are usually compared only with other algorithms. Finally, some future directions are suggested.

Binary arithmetic optimization algorithm for feature selection

Feature selection, widely used in data preprocessing, is a challenging problem as it involves hard combinatorial optimization. So far some meta-heuristic algorithms have shown effectiveness in solving hard combinatorial optimization problems. As the arithmetic optimization algorithm only performs well in dealing with continuous optimization problems, multiple binary arithmetic optimization algorithms (BAOAs) utilizing different strategies are proposed to perform feature selection. First, six algorithms are formed based on six different transfer functions by converting the continuous search space to the discrete search space. Second, in order to enhance the speed of searching and the ability of escaping from the local optima, six other algorithms are further developed by integrating the transfer functions and Lévy flight. Based on 20 common University of California Irvine (UCI) datasets, the performance of our proposed algorithms in feature selection is evaluated, and the results demonstrate that BAOA_S1LF is the most superior among all the proposed algorithms. Moreover, the performance of BAOA_S1LF is compared with other meta-heuristic algorithms on 26 UCI datasets, and the corresponding results show the superiority of BAOA_S1LF in feature selection. Source codes of BAOA_S1LF are publicly available at: https://www.mathworks.com/matlabcentral/fileexchange/124545-binary-arithmetic-optimization-algorithm.

Metaheuristic Optimization-Based Feature Selection for Imagery and Arithmetic Tasks: An fNIRS Study

In recent decades, the brain-computer interface (BCI) has emerged as a leading area of research. The feature selection is vital to reduce the dataset's dimensionality, increase the computing effectiveness, and enhance the BCI's performance. Using activity-related features leads to a high classification rate among the desired tasks. This study presents a wrapper-based metaheuristic feature selection framework for BCI applications using functional near-infrared spectroscopy (fNIRS). Here, the temporal statistical features (i.e., the mean, slope, maximum, skewness, and kurtosis) were computed from all the available channels to form a training vector. Seven metaheuristic optimization algorithms were tested for their classification performance using a k-nearest neighbor-based cost function: particle swarm optimization, cuckoo search optimization, the firefly algorithm, the bat algorithm, flower pollination optimization, whale optimization, and grey wolf optimization (GWO). The presented approach was validated based on an available online dataset of motor imagery (MI) and mental arithmetic (MA) tasks from 29 healthy subjects. The results showed that the classification accuracy was significantly improved by utilizing the features selected from the metaheuristic optimization algorithms relative to those obtained from the full set of features. All of the abovementioned metaheuristic algorithms improved the classification accuracy and reduced the feature vector size. The GWO yielded the highest average classification rates (p < 0.01) of 94.83 ± 5.5%, 92.57 ± 6.9%, and 85.66 ± 7.3% for the MA, MI, and four-class (left- and right-hand MI, MA, and baseline) tasks, respectively. The presented framework may be helpful in the training phase for selecting the appropriate features for robust fNIRS-based BCI applications.

A novel nonlinear time-varying sigmoid transfer function in binary whale optimization algorithm for descriptors selection in drug classification

In computational chemistry, the high-dimensional molecular descriptors contribute to the curse of dimensionality issue. Binary whale optimization algorithm (BWOA) is a recently proposed metaheuristic optimization algorithm that has been efficiently applied in feature selection. The main contribution of this paper is a new version of the nonlinear time-varying Sigmoid transfer function to improve the exploitation and exploration activities in the standard whale optimization algorithm (WOA). A new BWOA algorithm, namely BWOA-3, is introduced to solve the descriptors selection problem, which becomes the second contribution. To validate BWOA-3 performance, a high-dimensional drug dataset is employed. The proficiency of the proposed BWOA-3 and the comparative optimization algorithms are measured based on convergence speed, the length of the selected feature subset, and classification performance (accuracy, specificity, sensitivity, and f-measure). In addition, statistical significance tests are also conducted using the Friedman test and Wilcoxon signed-rank test. The comparative optimization algorithms include two BWOA variants, binary bat algorithm (BBA), binary gray wolf algorithm (BGWOA), and binary manta-ray foraging algorithm (BMRFO). As the final contribution, from all experiments, this study has successfully revealed the superiority of BWOA-3 in solving the descriptors selection problem and improving the Amphetamine-type Stimulants (ATS) drug classification performance.

Slime Mould Algorithm: A Comprehensive Survey of Its Variants and Applications

Meta-heuristic algorithms have a high position among academic researchers in various fields, such as science and engineering, in solving optimization problems. These algorithms can provide the most optimal solutions for optimization problems. This paper investigates a new meta-heuristic algorithm called Slime Mould algorithm (SMA) from different optimization aspects. The SMA algorithm was invented due to the fluctuating behavior of slime mold in nature. It has several new features with a unique mathematical model that uses adaptive weights to simulate the biological wave. It provides an optimal pathway for connecting food with high exploration and exploitation ability. As of 2020, many types of research based on SMA have been published in various scientific databases, including IEEE, Elsevier, Springer, Wiley, Tandfonline, MDPI, etc. In this paper, based on SMA, four areas of hybridization, progress, changes, and optimization are covered. The rate of using SMA in the mentioned areas is 15, 36, 7, and 42%, respectively. According to the findings, it can be claimed that SMA has been repeatedly used in solving optimization problems. As a result, it is anticipated that this paper will be beneficial for engineers, professionals, and academic scientists.

MMKE: Multi-trial vector-based monkey king evolution algorithm and its applications for engineering optimization problems

Monkey king evolution (MKE) is a population-based differential evolutionary algorithm in which the single evolution strategy and the control parameter affect the convergence and the balance between exploration and exploitation. Since evolution strategies have a considerable impact on the performance of algorithms, collaborating multiple strategies can significantly enhance the abilities of algorithms. This is our motivation to propose a multi-trial vector-based monkey king evolution algorithm named MMKE. It introduces novel best-history trial vector producer (BTVP) and random trial vector producer (RTVP) that can effectively collaborate with canonical MKE (MKE-TVP) using a multi-trial vector approach to tackle various real-world optimization problems with diverse challenges. It is expected that the proposed MMKE can improve the global search capability, strike a balance between exploration and exploitation, and prevent the original MKE algorithm from converging prematurely during the optimization process. The performance of the MMKE was assessed using CEC 2018 test functions, and the results were compared with eight metaheuristic algorithms. As a result of the experiments, it is demonstrated that the MMKE algorithm is capable of producing competitive and superior results in terms of accuracy and convergence rate in comparison to comparative algorithms. Additionally, the Friedman test was used to examine the gained experimental results statistically, proving that MMKE is significantly superior to comparative algorithms. Furthermore, four real-world engineering design problems and the optimal power flow (OPF) problem for the IEEE 30-bus system are optimized to demonstrate MMKE's real applicability. The results showed that MMKE can effectively handle the difficulties associated with engineering problems and is able to solve single and multi-objective OPF problems with better solutions than comparative algorithms.

Meta-Heuristic Optimization Algorithm-Based Hierarchical Intrusion Detection System

Numerous network cyberattacks have been launched due to inherent weaknesses. Network intrusion detection is a crucial foundation of the cybersecurity field. Intrusion detection systems (IDSs) are a type of machine learning (ML) software proposed for making decisions without explicit programming and with little human intervention. Although ML-based IDS advancements have surpassed earlier methods, they still struggle to identify attack types with high detection rates (DR) and low false alarm rates (FAR). This paper proposes a meta-heuristic optimization algorithm-based hierarchical IDS to identify several types of attack and to secure the computing environment. The proposed approach comprises three stages: The first stage includes data preprocessing, feature selection, and the splitting of the dataset into multiple binary balanced datasets. In the second stage, two novel meta-heuristic optimization algorithms are introduced to optimize the hyperparameters of the extreme learning machine during the construction of multiple binary models to detect different attack types. These are combined in the last stage using an aggregated anomaly detection engine in a hierarchical structure on account of the model’s accuracy. We propose a software machine learning IDS that enables multi-class classification. It achieved scores of 98.93, 99.63, 99.19, 99.78, and 0.01, with 0.51 for average accuracy, DR, and FAR in the UNSW-NB15 and CICIDS2017 datasets, respectively.

Boosting Whale Optimizer with Quasi-Oppositional Learning and Gaussian Barebone for Feature Selection and COVID-19 Image Segmentation

Whale optimization algorithm (WOA) tends to fall into the local optimum and fails to converge quickly in solving complex problems. To address the shortcomings, an improved WOA (QGBWOA) is proposed in this work. First, quasi-opposition-based learning is introduced to enhance the ability of WOA to search for optimal solutions. Second, a Gaussian barebone mechanism is embedded to promote diversity and expand the scope of the solution space in WOA. To verify the advantages of QGBWOA, comparison experiments between QGBWOA and its comparison peers were carried out on CEC 2014 with dimensions 10, 30, 50, and 100 and on CEC 2020 test with dimension 30. Furthermore, the performance results were tested using Wilcoxon signed-rank (WS), Friedman test, and post hoc statistical tests for statistical analysis. Convergence accuracy and speed are remarkably improved, as shown by experimental results. Finally, feature selection and multi-threshold image segmentation applications are demonstrated to validate the ability of QGBWOA to solve complex real-world problems. QGBWOA proves its superiority over compared algorithms in feature selection and multi-threshold image segmentation by performing several evaluation metrics.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s42235-022-00297-8.

A survey on binary metaheuristic algorithms and their engineering applications

This article presents a comprehensively state-of-the-art investigation of the engineering applications utilized by binary metaheuristic algorithms. Surveyed work is categorized based on application scenarios and solution encoding, and describes these algorithms in detail to help researchers choose appropriate methods to solve related applications. It is seen that transfer function is the main binary coding of metaheuristic algorithms, which usually adopts Sigmoid function. Among the contributions presented, there were different implementations and applications of metaheuristic algorithms, or the study of engineering applications by different objective functions such as the single- and multi-objective problems of feature selection, scheduling, layout and engineering structure optimization. The article identifies current troubles and challenges by the conducted review, and discusses that novel binary algorithm, transfer function, benchmark function, time-consuming problem and application integration are need to be resolved in future.

Automatic Parking Path Optimization Based on Immune Moth Flame Algorithm for Intelligent Vehicles

Automatic parking path optimization is a key point for automatic parking. However, it is difficult to obtain the smooth, accurate and optimal parking path by using traditional automatic parking optimization algorithms. So, based on the automatic parking path optimization model for cubic spline interpolation, an improved automatic parking path optimization based on the immune moth flame algorithm is proposed for intelligent vehicles. Firstly, to enhance the global optimization performance, an automatic parking path optimization model for cubic spline interpolation is designed by using shortest parking path as optimization target. Secondly, an improved immune moth flame algorithm (IIMFO) based on the immune mechanism, Gaussian mutation mechanism and opposition-based learning strategy is proposed, and an adaptive decreasing inertia weight coefficient is integrated into the moth flame algorithm so that these strategies can improve the balance quality between global search and local development effectively. Finally, the optimization results on the several common test functions show that the IIMFO algorithm proposed in this paper has higher optimization precision. Furthermore, the simulation and semi-automatic experiment results of automatic parking path optimization practical cases show that the improved automatic parking path optimization based on the immune moth flame algorithm for intelligent vehicles has a better optimization effect than that of the traditional automatic parking optimization algorithms.

Moth Flame Optimization: Theory, Modifications, Hybridizations, and Applications

The Moth flame optimization (MFO) algorithm belongs to the swarm intelligence family and is applied to solve complex real-world optimization problems in numerous domains. MFO and its variants are easy to understand and simple to operate. However, these algorithms have successfully solved optimization problems in different areas such as power and energy systems, engineering design, economic dispatch, image processing, and medical applications. A comprehensive review of MFO variants is presented in this context, including the classic version, binary types, modified versions, hybrid versions, multi-objective versions, and application part of the MFO algorithm in various sectors. Finally, the evaluation of the MFO algorithm is presented to measure its performance compared to other algorithms. The main focus of this literature is to present a survey and review the MFO and its applications. Also, the concluding remark section discusses some possible future research directions of the MFO algorithm and its variants.

Enhanced whale optimization algorithm for medical feature selection: A COVID-19 case study

The whale optimization algorithm (WOA) is a prominent problem solver which is broadly applied to solve NP-hard problems such as feature selection. However, it and most of its variants suffer from low population diversity and poor search strategy. Introducing efficient strategies is highly demanded to mitigate these core drawbacks of WOA particularly for dealing with the feature selection problem. Therefore, this paper is devoted to proposing an enhanced whale optimization algorithm named E-WOA using a pooling mechanism and three effective search strategies named migrating, preferential selecting, and enriched encircling prey. The performance of E-WOA is evaluated and compared with well-known WOA variants to solve global optimization problems. The obtained results proved that the E-WOA outperforms WOA's variants. After E-WOA showed a sufficient performance, then, it was used to propose a binary E-WOA named BE-WOA to select effective features, particularly from medical datasets. The BE-WOA is validated using medical diseases datasets and compared with the latest high-performing optimization algorithms in terms of fitness, accuracy, sensitivity, precision, and number of features. Moreover, the BE-WOA is applied to detect coronavirus disease 2019 (COVID-19) disease. The experimental and statistical results prove the efficiency of the BE-WOA in searching the problem space and selecting the most effective features compared to comparative optimization algorithms.

EGFAFS: A Novel Feature Selection Algorithm Based on Explosion Gravitation Field Algorithm

Feature selection (FS) is a vital step in data mining and machine learning, especially for analyzing the data in high-dimensional feature space. Gene expression data usually consist of a few samples characterized by high-dimensional feature space. As a result, they are not suitable to be processed by simple methods, such as the filter-based method. In this study, we propose a novel feature selection algorithm based on the Explosion Gravitation Field Algorithm, called EGFAFS. To reduce the dimensions of the feature space to acceptable dimensions, we constructed a recommended feature pool by a series of Random Forests based on the Gini index. Furthermore, by paying more attention to the features in the recommended feature pool, we can find the best subset more efficiently. To verify the performance of EGFAFS for FS, we tested EGFAFS on eight gene expression datasets compared with four heuristic-based FS methods (GA, PSO, SA, and DE) and four other FS methods (Boruta, HSICLasso, DNN-FS, and EGSG). The results show that EGFAFS has better performance for FS on gene expression data in terms of evaluation metrics, having more than the other eight FS algorithms. The genes selected by EGFAGS play an essential role in the differential co-expression network and some biological functions further demonstrate the success of EGFAFS for solving FS problems on gene expression data.

Binary Aquila Optimizer for Selecting Effective Features from Medical Data: A COVID-19 Case Study

Medical technological advancements have led to the creation of various large datasets with numerous attributes. The presence of redundant and irrelevant features in datasets negatively influences algorithms and leads to decreases in the performance of the algorithms. Using effective features in data mining and analyzing tasks such as classification can increase the accuracy of the results and relevant decisions made by decision-makers using them. This increase can become more acute when dealing with challenging, large-scale problems in medical applications. Nature-inspired metaheuristics show superior performance in finding optimal feature subsets in the literature. As a seminal attempt, a wrapper feature selection approach is presented on the basis of the newly proposed Aquila optimizer (AO) in this work. In this regard, the wrapper approach uses AO as a search algorithm in order to discover the most effective feature subset. S-shaped binary Aquila optimizer (SBAO) and V-shaped binary Aquila optimizer (VBAO) are two binary algorithms suggested for feature selection in medical datasets. Binary position vectors are generated utilizing S- and V-shaped transfer functions while the search space stays continuous. The suggested algorithms are compared to six recent binary optimization algorithms on seven benchmark medical datasets. In comparison to the comparative algorithms, the gained results demonstrate that using both proposed BAO variants can improve the classification accuracy on these medical datasets. The proposed algorithm is also tested on the real-dataset COVID-19. The findings testified that SBAO outperforms comparative algorithms regarding the least number of selected features with the highest accuracy.

An enhanced binary Rat Swarm Optimizer based on local-best concepts of PSO and collaborative crossover operators for feature selection

In this paper, an enhanced binary version of the Rat Swarm Optimizer (RSO) is proposed to deal with Feature Selection (FS) problems. FS is an important data reduction step in data mining which finds the most representative features from the entire data. Many FS-based swarm intelligence algorithms have been used to tackle FS. However, the door is still open for further investigations since no FS method gives cutting-edge results for all cases. In this paper, a recent swarm intelligence metaheuristic method called RSO which is inspired by the social and hunting behavior of a group of rats is enhanced and explored for FS problems. The binary enhanced RSO is built based on three successive modifications: i) an S-shape transfer function is used to develop binary RSO algorithms; ii) the local search paradigm of particle swarm optimization is used with the iterative loop of RSO to boost its local exploitation; iii) three crossover mechanisms are used and controlled by a switch probability to improve the diversity. Based on these enhancements, three versions of RSO are produced, referred to as Binary RSO (BRSO), Binary Enhanced RSO (BERSO), and Binary Enhanced RSO with Crossover operators (BERSOC). To assess the performance of these versions, a benchmark of 24 datasets from various domains is used. The proposed methods are assessed concerning the fitness value, number of selected features, classification accuracy, specificity, sensitivity, and computational time. The best performance is achieved by BERSOC followed by BERSO and then BRSO. These proposed versions are comparatively assessed against 25 well-regarded metaheuristic methods and five filter-based approaches. The obtained results underline their superiority by producing new best results for some datasets.

Gene selection using pyramid gravitational search algorithm

Genetics play a prominent role in the development and progression of malignant neoplasms. Identification of the relevant genes is a high-dimensional data processing problem. Pyramid gravitational search algorithm (PGSA), a hybrid method in which the number of genes is cyclically reduced is proposed to conquer the curse of dimensionality. PGSA consists of two elements, a filter and a wrapper method (inspired by the gravitational search algorithm) which iterates through cycles. The genes selected in each cycle are passed on to the subsequent cycles to further reduce the dimension. PGSA tries to maximize the classification accuracy using the most informative genes while reducing the number of genes. Results are reported on a multi-class microarray gene expression dataset for breast cancer. Several feature selection algorithms have been implemented to have a fair comparison. The PGSA ranked first in terms of accuracy (84.5%) with 73 genes. To check if the selected genes are meaningful in terms of patient’s survival and response to therapy, protein-protein interaction network analysis has been applied on the genes. An interesting pattern was emerged when examining the genetic network. HSP90AA1, PTK2 and SRC genes were amongst the top-rated bottleneck genes, and DNA damage, cell adhesion and migration pathways are highly enriched in the network.

Diagnosis of each main coronary artery stenosis based on whale optimization algorithm and stacking model

Cardiovascular disease is currently one of the diseases with high morbidity and mortality worldwide. One of the main types is coronary artery disease (CAD), which occurs when one or more of the three main arteries, the left anterior descending (LAD) artery, the left circumflex (LCX) artery, and the right coronary artery (RCA), are narrowed. In this paper, we introduce a computer-aided diagnosis model, which uses the k-nearest neighbor (KNN)-based whale optimization algorithm (WOA) for feature selection and combines stacking model for CAD diagnosis and prediction. In WOA, the values in the solution vectors are all continuous, and a threshold is set for binary-conversion to obtain the optimal feature subsets of each main coronary artery. Then we develop a two-layer stacking model based on the selected feature subsets to diagnosis LAD, LCX and RCA. By the proposed method, we select 17 features for each main artery diagnosis, and the classification accuracy on LAD, LCX, and RCA test sets is 89.68, 88.71 and 85.81%, respectively. On the Z-Alizadeh Sani dataset, we compare the proposed feature selection method with other metaheuristics and compare the performance of WOA based on different wrappers. The experimental results show that, the KNN-based WOA method selects the optimal feature subsets, and the classification performance of the stacking model is better than other machine learning algorithms.

Hybridizing of Whale and Moth-Flame Optimization Algorithms to Solve Diverse Scales of Optimal Power Flow Problem

The optimal power flow (OPF) is a practical problem in a power system with complex characteristics such as a large number of control parameters and also multi-modal and non-convex objective functions with inequality and nonlinear constraints. Thus, tackling the OPF problem is becoming a major priority for power engineers and researchers. Many metaheuristic algorithms with different search strategies have been developed to solve the OPF problem. Although, the majority of them suffer from stagnation, premature convergence, and local optima trapping during the optimization process, which results in producing low solution qualities, especially for real-world problems. This study is devoted to proposing an effective hybridizing of whale optimization algorithm (WOA) and a modified moth-flame optimization algorithm (MFO) named WMFO to solve the OPF problem. In the proposed WMFO, the WOA and the modified MFO cooperate to effectively discover the promising areas and provide high-quality solutions. A randomized boundary handling is used to return the solutions that have violated the permissible boundaries of search space. Moreover, a greedy selection operator is defined to assess the acceptance criteria of new solutions. Ultimately, the performance of the WMFO is scrutinized on single and multi-objective cases of different OPF problems including standard IEEE 14-bus, IEEE 30-bus, IEEE 39-bus, IEEE 57-bus, and IEEE118-bus test systems. The obtained results corroborate that the proposed algorithm outperforms the contender algorithms for solving the OPF problem.

An Optimized Framework for Breast Cancer Classification Using Machine Learning

Breast cancer, if diagnosed and treated early, has a better chance of surviving. Many studies have shown that a larger number of ultrasound images are generated every day, and the number of radiologists able to analyze this medical data is very limited. This often results in misclassification of breast lesions, resulting in a high false-positive rate. In this article, we propose a computer-aided diagnosis (CAD) system that can automatically generate an optimized algorithm. To train machine learning, we employ 13 features out of 185 available. Five machine learning classifiers were used to classify malignant versus benign tumors. The experimental results revealed Bayesian optimization with a tree-structured Parzen estimator based on a machine learning classifier for 10-fold cross-validation. The LightGBM classifier performs better than the other four classifiers, achieving 99.86% accuracy, 100.0% precision, 99.60% recall, and 99.80% for the FI score.

Binary Horse herd optimization algorithm with crossover operators for feature selection

This paper proposes a binary version of Horse herd Optimization Algorithm (HOA) to tackle Feature Selection (FS) problems. This algorithm mimics the conduct of a pack of horses when they are trying to survive. To build a Binary version of HOA, or referred to as BHOA, twofold of adjustments were made: i) Three transfer functions, namely S-shape, V-shape and U-shape, are utilized to transform the continues domain into a binary one. Four configurations of each transfer function are also well studied to yield four alternatives. ii) Three crossover operators: one-point, two-point and uniform are also suggested to ensure the efficiency of the proposed method for FS domain. The performance of the proposed fifteen BHOA versions is examined using 24 real-world FS datasets. A set of six metric measures was used to evaluate the outcome of the optimization methods: accuracy, number of features selected, fitness values, sensitivity, specificity and computational time. The best-formed version of the proposed versions is BHOA with S-shape and one-point crossover. The comparative evaluation was also accomplished against 21 state-of-the-art methods. The proposed method is able to find very competitive results where some of them are the best-recorded. Due to the viability of the proposed method, it can be further considered in other areas of machine learning.

Migration-Based Moth-Flame Optimization Algorithm

Moth–flame optimization (MFO) is a prominent swarm intelligence algorithm that demonstrates sufficient efficiency in tackling various optimization tasks. However, MFO cannot provide competitive results for complex optimization problems. The algorithm sinks into the local optimum due to the rapid dropping of population diversity and poor exploration. Hence, in this article, a migration-based moth–flame optimization (M-MFO) algorithm is proposed to address the mentioned issues. In M-MFO, the main focus is on improving the position of unlucky moths by migrating them stochastically in the early iterations using a random migration (RM) operator, maintaining the solution diversification by storing new qualified solutions separately in a guiding archive, and, finally, exploiting around the positions saved in the guiding archive using a guided migration (GM) operator. The dimensionally aware switch between these two operators guarantees the convergence of the population toward the promising zones. The proposed M-MFO was evaluated on the CEC 2018 benchmark suite on dimension 30 and compared against seven well-known variants of MFO, including LMFO, WCMFO, CMFO, CLSGMFO, LGCMFO, SMFO, and ODSFMFO. Then, the top four latest high-performing variants were considered for the main experiments with different dimensions, 30, 50, and 100. The experimental evaluations proved that the M-MFO provides sufficient exploration ability and population diversity maintenance by employing migration strategy and guiding archive. In addition, the statistical results analyzed by the Friedman test proved that the M-MFO demonstrates competitive performance compared to the contender algorithms used in the experiments.

MTV-MFO: Multi-Trial Vector-Based Moth-Flame Optimization Algorithm

The moth-flame optimization (MFO) algorithm is an effective nature-inspired algorithm based on the chemical effect of light on moths as an animal with bilateral symmetry. Although it is widely used to solve different optimization problems, its movement strategy affects the convergence and the balance between exploration and exploitation when dealing with complex problems. Since movement strategies significantly affect the performance of algorithms, the use of multi-search strategies can enhance their ability and effectiveness to solve different optimization problems. In this paper, we propose a multi-trial vector-based moth-flame optimization (MTV-MFO) algorithm. In the proposed algorithm, the MFO movement strategy is substituted by the multi-trial vector (MTV) approach to use a combination of different movement strategies, each of which is adjusted to accomplish a particular behavior. The proposed MTV-MFO algorithm uses three different search strategies to enhance the global search ability, maintain the balance between exploration and exploitation, and prevent the original MFO’s premature convergence during the optimization process. Furthermore, the MTV-MFO algorithm uses the knowledge of inferior moths preserved in two archives to prevent premature convergence and avoid local optima. The performance of the MTV-MFO algorithm was evaluated using 29 benchmark problems taken from the CEC 2018 competition on real parameter optimization. The gained results were compared with eight metaheuristic algorithms. The comparison of results shows that the MTV-MFO algorithm is able to provide competitive and superior results to the compared algorithms in terms of accuracy and convergence rate. Moreover, a statistical analysis of the MTV-MFO algorithm and other compared algorithms was conducted, and the effectiveness of our proposed algorithm was also demonstrated experimentally.

An Improved Moth-Flame Optimization Algorithm with Adaptation Mechanism to Solve Numerical and Mechanical Engineering Problems

Moth-flame optimization (MFO) algorithm inspired by the transverse orientation of moths toward the light source is an effective approach to solve global optimization problems. However, the MFO algorithm suffers from issues such as premature convergence, low population diversity, local optima entrapment, and imbalance between exploration and exploitation. In this study, therefore, an improved moth-flame optimization (I-MFO) algorithm is proposed to cope with canonical MFO's issues by locating trapped moths in local optimum via defining memory for each moth. The trapped moths tend to escape from the local optima by taking advantage of the adapted wandering around search (AWAS) strategy. The efficiency of the proposed I-MFO is evaluated by CEC 2018 benchmark functions and compared against other well-known metaheuristic algorithms. Moreover, the obtained results are statistically analyzed by the Friedman test on 30, 50, and 100 dimensions. Finally, the ability of the I-MFO algorithm to find the best optimal solutions for mechanical engineering problems is evaluated with three problems from the latest test-suite CEC 2020. The experimental and statistical results demonstrate that the proposed I-MFO is significantly superior to the contender algorithms and it successfully upgrades the shortcomings of the canonical MFO.

EWOA-OPF: Effective Whale Optimization Algorithm to Solve Optimal Power Flow Problem

The optimal power flow (OPF) is a vital tool for optimizing the control parameters of a power system by considering the desired objective functions subject to system constraints. Metaheuristic algorithms have been proven to be well-suited for solving complex optimization problems. The whale optimization algorithm (WOA) is one of the well-regarded metaheuristics that is widely used to solve different optimization problems. Despite the use of WOA in different fields of application as OPF, its effectiveness is decreased as the dimension size of the test system is increased. Therefore, in this paper, an effective whale optimization algorithm for solving optimal power flow problems (EWOA-OPF) is proposed. The main goal of this enhancement is to improve the exploration ability and maintain a proper balance between the exploration and exploitation of the canonical WOA. In the proposed algorithm, the movement strategy of whales is enhanced by introducing two new movement strategies: (1) encircling the prey using Levy motion and (2) searching for prey using Brownian motion that cooperate with canonical bubble-net attacking. To validate the proposed EWOA-OPF algorithm, a comparison among six well-known optimization algorithms is established to solve the OPF problem. All algorithms are used to optimize single- and multi-objective functions of the OPF under the system constraints. Standard IEEE 6-bus, IEEE 14-bus, IEEE 30-bus, and IEEE 118-bus test systems are used to evaluate the proposed EWOA-OPF and comparative algorithms for solving the OPF problem in diverse power system scale sizes. The comparison of results proves that the EWOA-OPF is able to solve single- and multi-objective OPF problems with better solutions than other comparative algorithms.

Breast cancer detection from thermal images using a Grunwald-Letnikov-aided Dragonfly algorithm-based deep feature selection method

Breast cancer is one of the deadliest diseases in women and its incidence is growing at an alarming rate. However, early detection of this disease can be life-saving. The rapid development of deep learning techniques has generated a great deal of interest in the medical imaging field. Researchers around the world are working on developing breast cancer detection methods using medical imaging. In the present work, we have proposed a two-stage model for breast cancer detection using thermographic images. Firstly, features are extracted from images using a deep learning model, called VGG16. To select the optimal subset of features, we use a meta-heuristic algorithm called the Dragonfly Algorithm (DA) in the second step. To improve the performance of the DA, a memory-based version of DA is proposed using the Grunwald-Letnikov (GL) method. The proposed two-stage framework has been evaluated on a publicly available standard dataset called DMR-IR. The proposed model efficiently filters out non-essential features and had 100% diagnostic accuracy on the standard dataset, with 82% fewer features compared to the VGG16 model.

Boosting Atomic Orbit Search Using Dynamic-Based Learning for Feature Selection

Feature selection (FS) is a well-known preprocess step in soft computing and machine learning algorithms. It plays a critical role in different real-world applications since it aims to determine the relevant features and remove other ones. This process (i.e., FS) reduces the time and space complexity of the learning technique used to handle the collected data. The feature selection methods based on metaheuristic (MH) techniques established their performance over all the conventional FS methods. So, in this paper, we presented a modified version of new MH techniques named Atomic Orbital Search (AOS) as FS technique. This is performed using the advances of dynamic opposite-based learning (DOL) strategy that is used to enhance the ability of AOS to explore the search domain. This is performed by increasing the diversity of the solutions during the searching process and updating the search domain. A set of eighteen datasets has been used to evaluate the efficiency of the developed FS approach, named AOSD, and the results of AOSD are compared with other MH methods. From the results, AOSD can reduce the number of features by preserving or increasing the classification accuracy better than other MH techniques.