A novel feature selection framework based on grey wolf optimizer for mammogram image analysis

Component value selection for active filters based on minimization of GSP and E12 compatible using Grey Wolf and Particle Swarm Optimization

An effective bearing fault diagnosis model based on machine learning is proposed in this study. The model can separate into three stages: feature extraction, feature selection, and classification. In the stage of feature extraction, multiresolution analysis (MRA) and fast Fourier transform (FFT) are applied to extract the features from the raw signal measured from the rotating machine. In the second stage, a powerful feature selection method is proposed and utilized in the stage of feature selection. The new feature selection method is based on grey wolf optimization (GWO) and heap-based optimizer (HBO) with new strategies combined. Finally, support vector machine (SVM) and linear discriminant analysis (LDA) are used as the classifier independently. To verify the capability of the proposed model, four different datasets are applied to test the model in this study, respectively University of California Irvine (UCI) benchmark dataset, bearing dataset, Case Western Reserve University (CWRU) benchmark dataset, and Machinery Failure Prevention Technology (MFPT) benchmark dataset. The proposed method is compared with the existing methods and can certify the robustness with the experiment results.

To optimize the hidden center matrix, Gaussian RMS width vector and the hidden-output weight matrix of RBF neural network, Grey Wolf Optimizer (GWO) and its several variants have been introduced. With the combination of the three parameters as the position vector of the grey wolf in GWO, selecting half of the average squared error as the optimizing object function, the RBF neural network based on GWO is named as RBF-GWO network. In the processing of training the parameters for RBF-GWO, the difference between the actual output of RBF-GWO network and the desired output value is considered as the guide of updating the position vector for the grey wolf, and in each iteration the optimal parameter values are stored in the position vector of Wolf α which will be returned to the RBF-GWO. The parameter training is completed until the end conditions of the iteration are satisfied. To verify the validity of RBF-GWO network, the continuous function approximation experiment and the chaotic synchronization anti-control experiment have been done in turn. Not only it is proved that the proposed RBF-GWO network is effective, but it is also found that the RBF-GWO network based on the WGWO has a relatively strong adaptive capacity in all experiments from the overall view.

Today, finding a viable solution for any real world problem focusing on combinatorial of problems is a crucial task. However, using optimisation techniques, a viable best solution for a specific problem can be obtained, developed and solved despite the existing limitations of the implemented technique. Furthermore, population based optimisation techniques are now a current interest and has spawned many new and improved techniques to rectify many engineering problems. One of these methods is the Grey Wolf Optimiser (GWO), which resembles the grey wolf’s leadership hierarchy and its hunting behavior in nature. The GWO adopts the hierarchical nature of grey wolfs and lists the best solution as alpha, followed by beta and delta in descending order. Additionally, its hunting technique of tracking, encircling and attacking are also modeled mathematically to find the best optimised solution. This paper presents the results from an extensive study of 83 published papers from previous studies related to GWO in various applications such as parameter tuning, economy dispatch problem, and cost estimating to name a few. A discussion on the properties of GWO algorithm and how it minimises the different problems in the different applications is presented, as well as an analysis on the research trend of GWO optimisation technique in various applications from year 2014 to 2017. Based on the literatures, it was observed that GWO has the ability to solve single and multi-objective problems efficiently due to its good local search criteria that performs exceptionally well for different problems and solutions.

Several bio-inspired meta-heuristic algorithms have been developed for solving real-world optimization problems, which mimic the social behavior of animals in nature. For control engineers, designing and tuning an optimal controller for a power converter has always been a challenge. This article presents a comparative analysis of the application of different bio-inspired meta-heuristic optimization techniques for designing a controller for a dc-dc boost converter circuit. This article has illustrated the development of a linear quadratic regulator (LQR) control framework with five different bio-inspired optimization algorithms, i.e., particle swarm optimization, genetic algorithm, grey wolf optimization(GWO), dragonfly algorithm, and whale optimization. GWO coupled with LQR has outperformed the other four optimization methods with 0\% overshoot, 0.48\% steady-state error, and the least execution time of 5.17 seconds. Hence, GWO has been proposed for power converter controller optimization due to its fast and efficient optimization performance. The controller design and optimization have been carried out in the Matlab/Simulink environment.

This study investigates a new fractional-order nonsingular terminal sliding mode control (FTSMC) leveraging a finite-time extended state observer, a simpler prescribed control, and hybrid grey wolf optimization (GWO) combined with whale optimization algorithm (WOA) for manipulator systems. The new FTSMC system is based on an improved fractional-order terminal sliding surface. Initially, the study experimentally optimizes the dynamic parameters and gains of the controller and the observer with the help of the newly developed GWO-WOA technique. As the next step, the uncertainties including optimization error and external disturbances are estimated by the finite-time extended state observer designed using the sliding mode dynamics. Experimental results of GWO-WOA optimization and joint position tracking for a self-designed articulated manipulator prove the efficacy of the proposed control scheme.

In this paper, we study the application of fuzzy set theory, genetic algorithms and rough set theory techniques to the control of the rectifier. A fuzzy adaptive control scheme with the aid of rough set theory via genetic algorithms (GAs) finding the center parameters in place of the classical control is proposed. On the one hand, genetic algorithm gets optimal parameters of an accurate domain model, which tunes the scaling factors of fuzzy adaptive control but is hardly established in a non-liner system. On the other hand, fuzzy adaptive control deals with the dynamics and complexity of responses from rectifier of a HVDC system in the operation points, by adjusting its control parameters with the aid of rough tuner adaptively. Our study includes a brief introduction to fuzzy control and rough set algorithms, both theory and application. We also evaluate the performance of rough fuzzy control by simulation in the paper. The focus of our experiments is on the constant current control in rectifier side of HVDC system. The result shows there are many improvements offered by the fuzzy control scheme based on rough set theory in comparison with the only fuzzy control scheme in the rectifier side of HVDC system.

The Path Restoration System in Mobile Ad hoc Network (MANET) was tough due to the changing environment. Data packets are lost if a link is broken while delivering information, and the system is vulnerable to various assaults. Considering this, we propose the Grey Wolf Optimization technique (GWO) to predict connection failure, link and node lifetime before broadcasting packets to avoid packet loss. To define the path, we used the Route Information Protocol (RIP). Following that, GWO is manually played; with this method, this research forecasts the node and lifetime, and achieves a packet delivery ratio of 0.7. The proposed Gray-Wolf algorithm achieves an efficient packet transmission rate and improves the early detection of links and node lifetimes to maintain path stability for data transmission. The proposed model reduces end-to-end delay, overhead, and packet drop. It improves the residual energy of nodes and the packet delivery ratio. Grey Wolf Optimization is one of many examining boosting methods activated by the grouping within the wolf family and the special hunting techniques used by grey wolves. As a result, the Grey Wolf optimization method was used to find the optimal result by mocking the overall characteristics of the grey wolf colony.

In this paper, we propose a new maximum entropy clustering algorithm by modified grey wolf optimizer (GWO), which modify the traditional GWO from twofold: First, nonlinear decay factor is constructed, which leads to flexible refined search; Second, proportional weights, which render the positions of distinct grey wolves, are adjusted adaptively according to the social hierarchy of them. Based on these two modifications, the rectified GWO could greatly improve both the accuracy and convergence rate. Experimental results on 12 benchmark functions demonstrate the effectiveness of those modifications. Furthermore, we utilize modified GWO to maximum entropy based fuzzy clustering problems. Experiments on 5 real datasets indicate the high performance and efficiency of the proposed approach.

Optimised autoencoder-based ensemble deep learning approaches for cyber-physical event classification utilizing synchrophasor PMU data

This study aims to map flood susceptibility in the Qaa’Jahran watersheds located in Dhamar, Yemen, using geoprocessing and computational techniques. Historical flood data and SAR imagery were used to monitor and create a flood inventory map. The artificial neutral network (ANN) was trained using a novel algorithm called GWO_LM, which is a hybridization between the Levenberg-Marquardt algorithm (LM) and Grey Wolf Optimizer (GWO) meta-heuristic algorithm and compared the results with state of art machine learning algorithms. The GWO_LM_ANN model exhibited excellent performance in the evaluation, achieving a precision of 97.92%, sensitivity of 100%, specificity of 100%, F1 score of 98.95%, accuracy of 98.75% and AUC of 98.48. This indicates that using GWO_LM for training ANN enhanced the searching process for the optimal weights, resulting in outperforming other state-of-the-art models. The findings hold significant implications for disaster preparedness and response in the Qaa’Jahran watersheds, enabling targeted and efficient non-structural solutions to mitigate the detrimental effects of flash floods in particularly sensitive locations. The use of the previously unexplored GWO_LM model represents a notable advancement in flood susceptibility assessment, surpassing traditional methods and offering novel insights to the existing literature.

Feature Selection (FS) has become one of the most active research topics in the area of data mining. It performs to remove redundant and noisy features from high-dimensional data sets. A good feature selection has several advantages for a learning algorithm such as reducing computational cost, increasing its classification accuracy and improving result comprehensibility. In the supervised FS methods various feature subsets are evaluated using an evaluation function or metric to select only those features which are related to the decision classes of the data under consideration. However, for many data mining applications, decision class labels are often unknown or incomplete, thus indicating the significance of unsupervised feature selection. However, in unsupervised learning, decision class labels are not provided. The problem is that not all features are important. Some of the features may be redundant, and others may be irrelevant and noisy. In this paper, a novel unsupervised feature selection in mammogram image, using rough set based measures, is proposed. A typical mammogram image processing system generally consists of mammogram image acquisition, preprocessing of image, segmentation, features extracted from the segmented mammogram image. The proposed method is used to select features from data set, the method is compared with existing rough set based supervised feature selection methods and classification performance of both methods are recorded and demonstrates the efficiency of the method.

Aiming at the difficulty of deploying wireless sensor networks (WSNs) on three-dimensional (3D) surfaces, based on the grey wolf optimizer (GWO), an enhanced version of the grey wolf optimizer is proposed for deploying WSNs on 3D surfaces, namely the enhanced grey wolf optimizer (EGWO), which is characterized by enhanced exploitation and exploration ability of the algorithm. The novelty of EGWO is that the grey wolf population is divided into two parts, one part is responsible for the outer-layer encircle and the other is responsible for the inner-layer encircle, and the introduction of Tent mapping. The purpose of this is to enhance the exploitation and exploration ability of the algorithm respectively, so as to improve the convergence and optimization precision of the algorithm. In addition, in terms of WSN deployment in 3D surfaces, this paper improves the means of determining the perceived blind zone. Meanwhile, a novel method to calculate the WSNs coverage area of simple and complex 3D surfaces is presented by combining the grid and integral of the 3D surfaces. The EGWO is favorably compared with the GWO and three existing variants of the grey wolf optimizer when testing on 12 well-known benchmark functions. The simulation experiment results show that compared with the existing algorithms, EGWO can provide a very competitive search result in terms of optimization precision and convergence performance. Finally, this paper applies EGWO to the 3D surface deployment of WSN. Simulations show that compared with the other three deployment algorithms, EGWO can improve the network coverage of WSN, which can save network deployment costs. In addition, the probability of network connectivity deployed by EGWO is higher, that is, EGWO can provide a better deployment solution.

PurposeSea Lion Optimization (SLnO) algorithm involves the ability of exploration and exploitation phases, and it is able to solve combinatorial optimization problems. For these reasons, it is considered a global optimizer. The scheduling operation is completed by imitating the hunting behavior of sea lions.Design/methodology/approachCloud computing (CC) is a type of distributed computing, contributory in a massive number of available resources and demands, and its goal is sharing the resources as services over the internet. Because of the optimal using of these services is everlasting challenge, the issue of task scheduling in CC is significant. In this paper, a task scheduling technique for CC based on SLnO and multiple-objective model are proposed. It enables decreasing in overall completion time, cost and power consumption; and maximizes the resources utilization. The simulation results on the tested data illustrated that the SLnO scheduler performed better performance than other state-of-the-art schedulers in terms of makespan, cost, energy consumption, resources utilization and degree of imbalance.FindingsThe performance of the SLnO, Vocalization of Whale Optimization Algorithm (VWOA), Whale Optimization Algorithm (WOA), Grey Wolf Optimization (GWO) and Round Robin (RR) algorithms for 100, 200, 300, 400 and 500 independent cloud tasks on 8, 16 and 32 VMs was evaluated. The results show that SLnO algorithm has better performance than VWOA, WOA, GWO and RR in terms of makespan and imbalance degree. In addition, SLnO exhausts less power than VWOA, WOA, GWO and RR. More precisely, SLnO conserves 5.6, 21.96, 22.7 and 73.98% energy compared to VWOA, WOA, GWO and RR mechanisms, respectively. On the other hand, SLnO algorithm shows better performance than the VWOA and other algorithms. The SLnO algorithm's overall execution cost of scheduling the cloud tasks is minimized by 20.62, 39.9, 42.44 and 46.9% compared with VWOA, WOA, GWO and RR algorithms, respectively. Finally, the SLnO algorithm's average resource utilization is increased by 6, 10, 11.8 and 31.8% compared with those of VWOA, WOA, GWO and RR mechanisms, respectively.Originality/valueTo the best of the authors’ knowledge, this work is original and has not been published elsewhere, nor is it currently under consideration for publication elsewhere.

In the context of rising demands for marine resource exploitation and scientific research, collaborative trajectory planning for multiple Autonomous Underwater Vehicles (AUVs) in complex underwater environments—marked by obstacles, ocean currents, and low visibility—remains a critical challenge. Although the Gray Wolf Optimization (GWO) algorithm has advanced multi-objective trajectory planning, it faces issues such as poor high-dimensional space adaptability, susceptibility to local optima, and insufficient constraint handling. To address these, this article proposes a multi-AUV trajectory planning algorithm (EA-GWO) based on evolutionary learning to improve GWO. The method optimizes multi-AUV trajectory planning by leveraging hierarchical population hunting behavior, integrating position update equations to prioritize population bootstrapping, and balancing exploration and exploitation via fitness-based population distribution. Experimental validation across general, ocean current, and threat environments compares EA-GWO with the traditional GWO and multiple population GWO (MP-GWO). For sailing time: in the general environment, EA-GWO reduces total time by 90.6% compared to GWO and 90.6% compared to MP-GWO; in the ocean current environment, it cuts time by 0.9% versus GWO and 2.4% versus MP-GWO; in the threat environment, it cuts time by 13.6% versus GWO and 14.9% versus MP-GWO. For sailing distance: in the general environment, EA-GWO shortens total distance by 9.8% compared to GWO and 3.4% compared to MP-GWO; in the ocean current environment, it reduces distance by 2.3% versus GWO and 4.9% versus MP-GWO; in the threat environment, it shortens distance by 5.5% versus GWO and 1.0% versus MP-GWO. In terms of convergence performance reflected by the fitness curve: across the three environments, EA-GWO demonstrates faster convergence speed. These results highlight that EA-GWO outperforms the other two algorithms in sailing time, distance, and convergence efficiency, verifying its effectiveness in real-time dynamic coordination and constraint handling for multi-AUV missions.