Particle Swarm Optimization Clustering Research Articles

Design of health information management model for elderly care using an advanced higher-order hybrid clustering algorithm from the perspective of sports and medicine integration.

In the context of integrating sports and medicine domains, the urgent resolution of elderly health supervision requires effective data clustering algorithms. This paper introduces a novel higher-order hybrid clustering algorithm that combines density values and the particle swarm optimization (PSO) algorithm. Initially, the traditional PSO algorithm is enhanced by integrating the Global Evolution Dynamic Model (GEDM) into the Distribution Estimation Algorithm (EDA), constructing a weighted covariance matrix-based GEDM. This adapted PSO algorithm dynamically selects between the Global Evolution Dynamic Model and the standard PSO algorithm to update population information, significantly enhancing convergence speed while mitigating the risk of local optima entrapment. Subsequently, the higher-order hybrid clustering algorithm is formulated based on the density value and the refined PSO algorithm. The PSO clustering algorithm is adopted in the initial clustering phase, culminating in class clusters after a finite number of iterations. These clusters then undergo the application of the density peak search algorithm to identify candidate centroids. The final centroids are determined through a fusion of the initial class clusters and the identified candidate centroids. Results showcase remarkable improvements: achieving 99.13%, 82.22%, and 99.22% for F-measure, recall, and precision on dataset S1, and 75.22%, 64.0%, and 64.4% on dataset CMC. Notably, the proposed algorithm yields a 75.22%, 64.4%, and 64.6% rate on dataset S, significantly surpassing the comparative schemes' performance. Moreover, employing the text vector representation of the LDA topic vector model underscores the efficacy of the higher-order hybrid clustering algorithm in efficiently clustering text information. This innovative approach facilitates swift and accurate clustering of elderly health data from the perspective of sports and medicine integration. It enables the identification of patterns and regularities within the data, facilitating the formulation of personalized health management strategies and addressing latent health concerns among the elderly population.

Flexible multi-objective particle swarm optimization clustering with game theory to address human activity discovery fully unsupervised

Human activity recognition is a crucial field of study, but current approaches often require ground truth labels, which are not always available. We propose a new method called the Flexible Multi-Objective Particle swarm optimization clustering method based on Game theory (FMOPG), which can identify human activities without any supervision. Unlike traditional clustering methods that require an estimate of the number of clusters and are often inaccurate, FMOPG handles varying cluster numbers with an incremental technique, selecting clusters with good connectivity and separation. We enhance Particle Swarm Optimization (PSO) with mean-shift vectors for faster convergence and better handling of non-spherical clusters. Employing multi-objective optimization and Gaussian mutation, FMOPG provides a range of optimal solutions. We map the optimization problem to game theory to select the best solution based on different criteria. A smart grid-based method is proposed for population initialization, reducing variance and improving reliability. FMOPG outperforms state-of-the-art methods, improving clustering accuracy by 3.65%. Moreover, the incremental technique has improved clustering time by 71.18%.

A credit scoring ensemble model incorporating fuzzy clustering particle swarm optimization algorithm

The emergence of credit has generated a wealth of data on consumer lending behavior. In recent years, financial institutions have also started to use such data to make informed lending decisions based on fine-grained customer data, but conventional risk assessment models are inadequate in meeting the risk control requirements of the financial industry. Therefore, this paper proposes a credit scoring ensemble model incorporating fuzzy clustering particle swarm optimization (PSO) algorithm to obtain better credit risk prediction capability. First, a weighted outlier detection method based on the Induced Ordered Weighted Average Operator is proposed to preprocess the data to reduce noisy data’s misleading effect on model training. Then, an undersampling method combined with fuzzy clustering PSO is proposed to overcome the negative effect of category imbalance on model training by resampling the data. In addition, a hyperparameter optimization framework is introduced to adaptively adjust important parameters in the ensemble model considering the impact of parameter settings on the training performance of the model. Based on the evaluation metrics of F-score, AUC, and Kappa coefficient, an empirical analysis was conducted on five credit risk datasets. The results show that the proposed method outperforms the comparative model with an improvement of 10% to 50% in terms of F-score and AUC. The highest achieved F-score is 0.9488, and the maximum AUC is 0.9807, demonstrating the effectiveness of the proposed method. The kappa coefficient results indicate a high level of consistency in the predicted classification results of the model.

Energy Efficient Clustering Using Improved Particle Swarm Optimization in Wireless Sensor Networks

The Wireless Sensor Network (WSN) includes many low-cost nodes that have the capacity to perceive, operate, and communicate wirelessly. WSN can spread the information to all around through a cooperative node approach. It also has many advantages in terms of both cost and cooperative intelligence. In a wireless sensor network, nodes have limited energy resources, so their life cycle is considered as one of the main concerns about wireless sensor networks. Energy efficiency grouping and routing are two well-known issues in optimization that have been widely studied in order to increase the lifetime of wireless sensor networks. In this paper, an improved particle swarm optimization (IPSO) clustering algorithm for energy efficiency network management is introduced in order to find a route for creating optimal clusters. To evaluate the efficiency of the proposed clustering algorithm, this algorithm is simulated and compared with the particle swarm optimization(PSO) algorithm based on parameters such as network energy, number of live nodes and network life.

Human Activity Discovery With Automatic Multi-Objective Particle Swarm Optimization Clustering With Gaussian Mutation and Game Theory

Despite many advances in Human Activity Recognition (HAR), most existing works are conducted with supervision. Supervised methods rely on labeled training data. However, obtaining labeled data is difficult, costly, and time-consuming. In this paper, we introduce an automatic multi-objective particle swarm optimization clustering based on Gaussian mutation and game theory (MOPGMGT) to provide fully unsupervised human activity discovery. Furthermore, we map the multi-objective clustering problem to game theory to get the best optimal solution. The proposed algorithm can accurately find the number of activities without any prior knowledge. Multi-objective optimization problems typically cannot have a single optimal solution. We solve this problem by applying, Nash Equilibrium (NE) to the pareto front as the decision-making for choosing the best solution. NE does not just look for the best solution but tries to optimize the final solution by considering the effect of choosing each of the solutions as the best solution on the other solutions and one with the best impact is chosen. Moreover, a Gaussian mutation is applied on the pareto front to avoid premature convergence. As far as we know, this is the first time that human activity discovery is performed fully unsupervised, and a multi-objective PSO is mapped to the game theory space for finding the best solution. Experiments on six challenging human activity datasets demonstrate the capability of the proposed approach in achieving the best accuracy in human activity discovery and determining the optimal number of clusters. In comparison to well-known multi-objective algorithms, the MOPGMGT significantly improves the clustering outcomes on six benchmark clustering datasets.

Ship Target Detection Method in Synthetic Aperture Radar Images Based on Block Thumbnail Particle Swarm Optimization Clustering

Ship target detection is an important application of synthetic aperture radar (SAR) imaging remote sensing in ocean monitoring and management. However, SAR imaging is a form of coherence imaging, meaning that there is a large amount of speckle noise in each SAR image. This seriously affects the detection of an SAR image ship target when the fuzzy C-means (FCM) clustering method is used, resulting in numerous errors and incomplete detection. It is also associated with a slow detection speed, which easily falls into the local minima. To overcome these issues, a new method based on block thumbnail particle swarm optimization clustering (BTPSOC) was proposed for SAR image ship target detection. The BTPSOC algorithm uses block thumbnails to segment the main pixels, which improves the resistance to noise interference and segmentation accuracy, enhances the ability to process different types of SAR images, and reduces the runtime. When particle swarm optimization (PSO) technology is used to optimize the FCM clustering center, global optimization is achieved, the clustering performance is improved, the risk of falling into the local minima is overcome, and the stability is improved. The SAR images from two datasets containing ship targets were used in verification experiments. The experimental results show that the BTPSOC algorithm can effectively detect the ship target in SAR images and that it maintains good integrity with regard to the detailed information from the target region. At the same time, experiments comparing the deep convolution neural network (CNN) and constant false alarm rate (CFAR) were conducted.

Prediction of anemia with a particle swarm optimization-based approach

Healthcare enables the maintenance of health through some physical and mental care for the prevention, diagnosis and treatment of disease. Diagnosis of anemia, one of the most common health problems of the age, is also very ambitious. Whereas, pathological individuals could be predicted through various biomedical variables using some appropriate methods. In order to estimate these individuals just by taking into account biological data, particle swarm optimization (PSO) and support vector machine (SVM) clustering techniques have been merged (PSO-SVM). In this respect, the dataset provided has been divided into five clusters based on anemia types consisting of 539 subjects in total, and the anemia values of each subject have been recorded according to corresponding biomedical variables taken as independent parameters. The findings of the PSO-SVM method have been compared to the results of the SVM algorithm. The hybrid PSO-SVM method has proven to be quite effective, particularly in terms of the high predictability of clustered disease types. it is possible to lead the successful creation of appropriate treatment programs for diagnosed patients without overlooking or wasting time during treatment.

An Extended Membrane System with Monodirectional Tissue-like P Systems and Enhanced Particle Swarm Optimization for Data Clustering

In order to establish a highly efficient P system for resolving clustering problems and overcome the computation incompleteness and implementation difficulty of P systems, an attractive clustering membrane system, integrated with enhanced particle swarm optimization (PSO) based on environmental factors and crossover operators and a distributed parallel computing model of monodirectional tissue-like P systems (MTP), is constructed and proposed, which is simply named ECPSO-MTP. In the proposed ECPSO-MTP, two kinds of evolution rules for objects are defined and introduced to rewrite and modify the velocity of objects in different elementary membranes. The velocity updating model uses environmental factors based on partitioning information and randomly replaces global best to improve the clustering performance of ECPSO-MTP. The crossover operator for the position of objects is based on given objects and other objects with crossover probability and is accomplished through the hybridization of the global best of elementary membranes to reject randomness. The membrane structure of ECPSO-MTP is abstracted as a network structure, and the information exchange and resource sharing between different elementary membranes are accomplished by evolutional symport rules with promoters for objects of MTP, including forward and backward communication rules. The evolution and communication mechanisms in ECPSO-MTP are executed repeatedly through iteration. At last, comparison experiments, which are conducted on eight benchmark clustering datasets from artificial datasets and the UCI Machine Learning Repository and eight image segmentation datasets from BSDS500, demonstrate the effectiveness of the proposed ECPSO-MTP.

Establishing the Forecasting Model with Time Series Data Based on Graph and Particle Swarm Optimization

In recent years, a wide variety of fuzzy time series (FTS) forecasting models have been created and recommended to handle the complicated and ambiguous challenges relating to time series data from real-world sources. However, the accuracy of a model is problem-specific and varies across data sets. But a model's precision varies between different data sets and depends on the situation at hand. Even though many models assert that they are better than statistics and a single machine learning-based model, increasing forecasting accuracy is still a challenging task. In the fuzzy time series models, the size of the intervals and the fuzzy relationship groups are thought to be crucial variables that affect the model's forecasting abilities. This study offers a hybrid FTS forecasting model that makes use of both the graph-based clustering technique (GBC) and particle swarm optimization (PSO) for adjusting interval lengths in the universe of discourse (UoD). The suggested model's forecasting results have been compared to those provided by other current models on a dataset of enrollments at the University of Alabama. For all orders of fuzzy relationships, the suggested model outperforms its counterparts in terms of forecasting accuracy.

Establishing the Forecasting Model with Time Series Data Based on Graph and Particle Swarm Optimization

In recent years, a wide variety of fuzzy time series (FTS) forecasting models have been created and recommended to handle the complicated and ambiguous challenges relating to time series data from real-world sources. However, the accuracy of a model is problem-specific and varies across data sets. But a model’s precision varies between different data sets and depends on the situation at hand. Even though many models assert that they are better than statistics and a single machine learning-based model, increasing forecasting accuracy is still a challenging task. In the fuzzy time series models, the size of the intervals and the fuzzy relationship groups are thought to be crucial variables that affect the model’s forecasting abilities. This study offers a hybrid FTS forecasting model that makes use of both the graph-based clustering technique (GBC) and particle swarm optimization (PSO) for adjusting interval lengths in the universe of discourse (UoD). The suggested model’s forecasting results have been compared to those provided by other current models on a dataset of enrollments at the University of Alabama. For all orders of fuzzy relationships, the suggested model outperforms its counterparts in terms of forecasting accuracy.

A Multivariate Time Series Anomaly Detection Method Based on Clustered Particle Swarm Optimization

Due to the advent of 5G and the integration of sensors, sensor nodes need to collect data from multiple sources simultaneously.Traditional anomaly detection methods for single attribute time series detect multivariate data, low detection accuracy, and significant node energy consumption. To avoid these problems, this paper provides an improved FCM multivariate time series clustering method based on the sliding window. Based on the CPSO optimal weight, the reconstruction error realizes anomaly detection. Experiments on different datasets demonstrate the effectiveness of the algorithm. Moreover, compared with the three classical methods, the results show that the proposed algorithm has higher detection accuracy and accuracy.

Equivalent Modeling of LVRT Characteristics for Centralized DFIG Wind Farms Based on PSO and DBSCAN

As large-scale wind turbines are connected to the grid, modeling studies of wind farms are essential to the power system dynamic research. Due to the large number of wind turbines in the wind farm, detailed modeling of each wind turbine leads to high model complexity and low simulation efficiency. An equivalent modeling method for the wind farm is needed to reduce the complexity. For wind farms with widely used doubly-fed induction generators (DFIGs), the existing equivalent studies mainly focus on such continuous control parts as electrical control. These methods are unsuitable for the low voltage ride through (LVRT) part which is discontinuous due to switching control. Based on particle swarm optimization (PSO) and density-based spatial clustering of applications (DBSCAN), this paper proposes an equivalent method for LVRT characteristics of wind farms. Firstly, the multi-turbine equivalent model of the wind farm is established. Each wind turbine in the model represents a cluster of wind turbines with similar voltage variation characteristics. A single equivalent transmission line connects all wind turbines to the power grid. By changing the terminal voltage threshold to enter LVRT, each equivalent turbine can be in different LVRT states. Secondly, an LVRT parameter optimization method based on PSO is used to obtain the dynamic parameters of the equivalent wind turbines. This method of parameter optimization is applicable to the equivalent of LVRT parameters. Thirdly, a clustering method based on DBSCAN is used to obtain suitable clusters of wind turbines. This clustering method can classify wind turbines with similar electrical distances into the same cluster. Finally, two examples are set up to verify the proposed method.

Wireless Sensor Network Lifetime Extension via K-Medoids and MCDM Techniques in Uncertain Environment

In this study, the multi-criteria decision-making (MCDM) technique is used in collaboration with K-medoids clustering to establish a novel algorithm for extending the lifetime of wireless sensor networks (WSNs) in the presence of uncertainty. One of the most important problems in WSNs is the energy consumption. Furthermore, extending the network lifetime in WSNs is highly dependent on selecting the appropriate cluster heads (CHs), and this can be a challenging task for the decision makers. In addition, parameters associated with WSNs are completely unexpected due to uncertainty. Therefore, after proposing K-medoids clustering and a MCDM technique, we have developed a novel algorithm for extending the lifetime of WSNs. As criteria, we have taken into account four important aspects of the proposed WSN: the distance from sink, average distance of cluster nodes, reliability of cluster and residual energy. To represent uncertain parameters in this work, we have considered triangular fuzzy numbers (TFNs). Finally, an experiment involving a WSN under uncertainty was investigated, and the findings have been graphically displayed. In this research, it has been observed that the proposed strategy with the novel algorithm exhibits 42% greater network lifetime as compared with a hybrid energy efficient distributed (HEED) algorithm and 11% and 18% with respect to optimal clustering artificial bee colony (OCABC) and particle swarm optimization (PSO) applied to a clustering optimization problem. We have also conducted statistical hypotheses for the purpose of confirming the presented outcomes.

Enhancing Thyroid Patient Dietary Management with an Optimized Recommender System based on PSO and K-means

With the increasing prevalence of thyroid-related disorders, the need for personalized dietary recommendations for thyroid patients becomes paramount. This study introduces an optimized recommender system that leverages Particle Swarm Optimization (PSO) and K-means Clustering to provide tailored food recommendations for individuals with thyroid conditions. The proposed recommender system utilizes historical patient data encompassing dietary habits, thyroid health metrics, and treatment outcomes to identify relevant patterns and clusters within the dataset. The system recommends food items that are rich in essential nutrients beneficial for thyroid health while avoiding those that may be harmful. Here utilizes a comprehensive dataset of food nutrient values to develop the recommender system. The PSO algorithm is employed to optimize the clustering process in K-means, leading to more effective and accurate cluster formation. Incorporating PSO better captures the optimal centroids of the clusters, resulting in enhanced recommendations for thyroid patients. The performance of the proposed system is evaluated using the Davies-Bouldin index score, which measures the compactness and separability of the clusters. The lower the Davies-Bouldin index, the better the quality of the clustering. To further assess the effectiveness of the PSO-K-means approach, a comparison is made with other algorithms. The comparison is made based on the Intraclass Inertia of each algorithm. The experimental results demonstrate that the PSO-K-means algorithm outperforms other optimization algorithms in terms of the Davies-Bouldin index score. The optimized recommender system provides more accurate and relevant food recommendations for thyroid patients, considering essential nutrients and avoiding harmful ones.

Energy-Aware Dynamic Trajectory Planning for UAV-Enabled Data Collection in mMTC Networks

A fundamental design problem for massive machine-type communication (mMTC) networks is efficient data collection from the machine-type communication devices (MTCDs), which is the subject of investigation in this paper. An unmanned aerial vehicle (UAV) being deployed to facilitate data collection from MTCDs is considered. Taking into account the limited energy for both the UAV and MTCDs, a problem of minimizing the total energy consumption subject to completion of the data collection tasks by planning the UAV trajectory is formulated. A Global Optimum (GOP) trajectory can be obtained for a UAV serving all the MTCDs simultaneously if the UAV’s flying altitude is larger than <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sqrt {3}$ </tex-math></inline-formula> times its maximum service radius. However, communication energy efficiency drops as the UAV’s altitude increases. Clustering-based service strategies and dynamic trajectory planning algorithms, namely clustered GOP (C-GOP) and clustered particle swarm optimization (C-PSO), are proposed to overcome the above issue. The data collection efficiency is maximized by locating the optimal UAV hovering point for each serving MTCD cluster, which is dynamically adjusted with the UAV hovering position until all MTCDs are served. It is shown that the GOP is the optimal strategy for a small number of MTCDs concentrated in a small area. While for large number of MTCDs or task area, the clustered algorithms are more favorable from energy efficiency, complexity and scalability perspectives.

A new decomposition ensemble model for stock price forecasting based on system clustering and particle swarm optimization

Accurate forecasting of stock prices has been a challenge in the securities market, while the stock price time series tend to be non-stationary, non-linear, and highly noisy. At present, the traditional method of decomposition and ensemble can improve the forecasting accuracy, but it increases the computational complexity of forecasting, which may introduce additional forecasting errors. In order to address these problems, this paper proposes a novel decomposition and reconstruction model based on system clustering method (SCM) and particle swarm optimization (PSO). Firstly, the system uses the ensemble empirical mode decomposition (EEMD) technique to pre-process stock prices. Furthermore, the complexity of each modality is then measured using the sample entropy method. In addition, the modes are systematically clustered and reconstructed according to three patterns of short, medium, and long-term market changes. Apart from that, subsequences of four different characteristics at coarse granularity (containing the residual term acquired from the mode decomposition) are obtained, thus reducing the complexity of the prediction calculation. Meanwhile, a PSO optimized long short-term memory (LSTM) neural network or support vector regression (SVR) model is used to predict the sub-series with different characteristics separately. The updated model combines decomposition, forecasting, and ensemble steps with a system clustering approach and the idea of “divide and conquer” from a multi-granularity perspective, which not only changes the working mechanism of traditional decomposition-ensemble forecasting models but also realizes a new theoretical model for forecasting stock prices. According to experimental results, the proposed model has higher forecasting accuracy than other current forecasting methods, while it is more consistent with the dynamic stock market’s physical significance.

Ride Sharing Using Dynamic Rebalancing with PSO Clustring A Case Study of NYC

The shared vehicle can improve the efficiencyof urban mobility by reducing car ownership and parkingdemand. Existing rebalancing research divides thesystem coverage area into defined geographical zones,but this is achieved statically at system design time,limiting the system’s adaptability to evolve. In the currentstudy, a method has been proposed for rebalancingunoccupied vehicles in real-time while considering travelrequests, using a bio-inspired method known as ParticleSwarm Optimization clustering (PSO-Clustering). Thesolution was examined using data on taxi usage inNew York City, first looking at the traditional system (noridesharing, no rebalancing), then carpooling, and finallyof both ridesharing and rebalancing.

Informative SNP Selection Based on a Fuzzy Clustering and Improved Binary Particle Swarm Optimization Algorithm.

Single-nucleotide polymorphism (SNP) involves the replacement of a single nucleotide in a deoxyribonucleic acid (DNA) sequence and is often linked to the development of specific diseases. Although current genotyping methods can tag SNP loci within biological samples to provide accurate genetic information for a disease associated, they have limited prediction accuracy. Furthermore, they are complex to perform and may result in the prediction of an excessive number of tag SNP loci, which may not always be associated with the disease. Therefore in this manuscript, we aimed to evaluate the impact of a newly optimized fuzzy clustering and binary particle swarm optimization algorithm (FCBPSO) on the accuracy and running time of informative SNP selection. Fuzzy clustering and FCBPSO were first applied to identify the equivalence relation and the candidate tag SNP set to reduce the redundancy between loci. The FCBPSO algorithm was then optimized and used to obtain the final tag SNP set. The prediction performance and running time of the newly developed model were compared with other traditional methods, including NMC, SPSO, and MCMR. The prediction accuracy of the FCBPSO algorithm was always higher than that of the other algorithms especially as the number of tag SNPs increased. However, when the number of tag SNPs was low, the prediction accuracy of FCBPSO was slightly lower than that of MCMR (add prediction accuracy values for each algorithm). However, the running time of the FCBPSO algorithm was always lower than that of MCMR. FCBPSO not only reduced the size and dimension of the optimization problem but also simplified the training of the prediction model. This improved the prediction accuracy of the model and reduced the running time when compared with other traditional methods.

Performance evaluation of the particle swarm optimization for clustering based on different parameter selection

Particle Swamp Optimization (PSO) is an effective method for solving a wide range of problems. However, the most existing PSO algorithms easily trap into local optima when solving complex multimodal function optimization problems. In this paper, we explain the importance of PSO algorithm’s general purposes to optimize strategy which has various parameters that decide its conduct and viability in advancing a given issue. This study gives a rundown of the best selections of parameters for different advancement situations which should enable the specialist to accomplish better outcomes with less exertion. In this paper, we define an important in the PSO algorithm, the parameters and how to apply this algorithm to different type of datasets online and offline. Repeat Consumption Matrices Dataset has been tested with PSO algorithm to define the number of clusters using selection of parameters. The results are then having been compared with the Genetic Algorithm (GA) where the algorithms have been tested based on the number of cluster and DB index for each of the data. Based on the results, it is shown that the PSO algorithm gives good and efficient results compared to GA algorithm. The output of the research is a PSO-based clustering algorithm that can be used in data mining by providing accurate and robust results in clustering, which can be realized in web search engines and automatic doc-ument organization.

Fitness-Based Grey Wolf Optimizer Clustering Method for Spam Review Detection

Customers nowadays rely heavily on online reviews when making buying decisions. Various internet websites, including Amazon, Yelp, Google Plus, BookMyShow, Facebook, Twitter, and others, allow users to generate massive amounts of data. The information is gathered through feedback/reviews, comments, and tweets. Companies can leverage this information to improve the quality of their products. Spam reviews are created pretentiously by some businesses and people to promote or degrade the popularity of any product, organization, or person due to their reliance on these online reviews. Thus, identifying spam or nonspam review by the naked eye is nearly impossible. Classifying the reviews manually is also highly speculative. Hence, to overcome this issue, a fitness-based Grey Wolf Optimizer (FGWOK) clustering method is proposed in this paper to identify spam reviews. The fitness-based grey wolf optimization (GWO) is used to obtain the optimal cluster heads in the proposed method. In the fitness-based GWO, the position of the grey wolves is updated in two phases. In the first phase, all the search agents update their position using the contemporary GWO, and in the second phase, the fitness is evaluated to update the position. To prove that the proposed strategy is effective, three spam data sets, namely synthetic spam reviews, movie reviews, and Yelp hotel and restaurant reviews, have been used in our work. The reported results are compared with the existing state-of-art metaheuristic clustering methods like a genetic algorithm (GA), differential evolution (DE), particle swarm optimization (PSO), cuckoo search (CS), k-Means, and artificial bee colony clustering (ABCK) method. The experimental and statistical analysis results show that the proposed FGWOK algorithm outperforms current methodologies.