Competitive Swarm Optimizer Research Articles

Fr-WCSO- DRN: Fractional Water Cycle Swarm Optimizer-Based Deep Residual Network for Pulmonary Abnormality Detection from Respiratory Sound Signals

Respiratory sounds disclose significant information regarding the lungs of patients. Numerous methods are developed for analyzing the lung sounds. However, clinical approaches require qualified pulmonologists to diagnose such kind of signals appropriately and are also time consuming. Hence, an efficient Fractional Water Cycle Swarm Optimizer-based Deep Residual Network (Fr-WCSO-based DRN) is developed in this research for detecting the pulmonary abnormalities using respiratory sounds signals. The proposed Fr-WCSO is newly designed by the incorporation of Fractional Calculus (FC) and Water Cycle Swarm Optimizer WCSO. Meanwhile, WCSO is the combination of Water Cycle Algorithm (WCA) with Competitive Swarm Optimizer (CSO). The respiratory input sound signals are pre-processed and the important features needed for the further processing are effectively extracted. With the extracted features, data augmentation is carried out for minimizing the over fitting issues for improving the overall detection performance. Once data augmentation is done, feature selection is performed using proposed Fr-WCSO algorithm. Finally, pulmonary abnormality detection is performed using DRN where the training procedure of DRN is performed using the developed Fr-WCSO algorithm. The developed method achieved superior performance by considering the evaluation measures, namely True Positive Rate (TPR), True Negative Rate (TNR) and testing accuracy with the values of 0.963, 0.932, and 0.948, respectively. Moreover, the testing accuracy value achieved by the Random Forest classifier, machine learning, DNN, CNN, WCSO-based HAN, and developed Fr-WCSO-based DRN is 0.753, 0.797, 0.844, 0.887, 0.929, and 0.948. While analyzing the results that are tabulated, it is clear that the developed Fr-WCSO-based DRN computed a higher TPR of 0.963, higher TNR of 0.932 using dataset-1, and higher testing accuracy of 0.948 using dataset-2, respectively. The effectual results are obtained as the model is well trained with the proposed Fr-WCSO and hence increasing the learning rate of the Deep Residual Network.

Cost-Sensitive Metaheuristic Optimization-Based Neural Network with Ensemble Learning for Financial Distress Prediction

Financial distress prediction is crucial in the financial domain because of its implications for banks, businesses, and corporations. Serious financial losses may occur because of poor financial distress prediction. As a result, significant efforts have been made to develop prediction models that can assist decision-makers to anticipate events before they occur and avoid bankruptcy, thereby helping to improve the quality of such tasks. Because of the usual highly imbalanced distribution of data, financial distress prediction is a challenging task. Hence, a wide range of methods and algorithms have been developed over recent decades to address the classification of imbalanced datasets. Metaheuristic optimization-based artificial neural networks have shown exciting results in a variety of applications, as well as classification problems. However, less consideration has been paid to using a cost sensitivity fitness function in metaheuristic optimization-based artificial neural networks to solve the financial distress prediction problem. In this work, we propose ENS_PSONNcost and ENS_CSONNcost: metaheuristic optimization-based artificial neural networks that utilize a particle swarm optimizer and a competitive swarm optimizer and five cost sensitivity fitness functions as the base learners in a majority voting ensemble learning paradigm. Three extremely imbalanced datasets from Spanish, Taiwanese, and Polish companies were considered to avoid dataset bias. The results showed significant improvements in the g-mean (the geometric mean of sensitivity and specificity) metric and the F1 score (the harmonic mean of precision and sensitivity) while maintaining adequately high accuracy.

Large-scale multiobjective optimization with adaptive competitive swarm optimizer and inverse modeling

Competitive swarm optimizer (CSO) is an efficient algorithm for solving larger-scale multiobjective optimization problems (LSMOPs). However, the winners are not updated in original algorithm, and the random parameters model affects the convergence speed. To improve the performance of CSO for solving LSMOPs, an adaptive competitive swarm optimizer with inverse modeling is developed. In the method, an adaptive parameter model is designed to accelerate the convergence speed when the population not traps in local convergence, and the random parameter model is used to help the losers jump out of local convergence when the population traps in stagnation. Moreover, inverse modeling is used to update the winners, and the number of them is maintained by a new designed environment selection method. The exploitation ability of algorithm is improved and the drawback that convergence speed of the algorithm might be slowed by few winners in the early stage of evolution is avoided. In addition, an adjacent individual competition method is proposed to improve the distribution of solutions. Finally, the proposed algorithm is tested on nine benchmark optimization problems, and the results indicate that the average ranks in terms of IGD and HV of the proposed algorithm outperform those of seven compared algorithms.

A self‐organizing weighted optimization based framework for large‐scale multi‐objective optimization

The solving of large-scale multi-objective optimization problem (LSMOP) has become a hot research topic in evolutionary computation. To better solve this problem, this paper proposes a self-organizing weighted optimization based framework, denoted S-WOF, for addressing LSMOPs. Compared to the original framework, there are two main improvements in our work. Firstly, S-WOF simplifies the evolutionary stage into one stage, in which the evaluating numbers of weighted based optimization and normal optimization approaches are adaptively adjusted based on the current evolutionary state. Specifically, regarding the evaluating number for weighted based optimization (i.e., t1), it is larger when the population is in the exploitation state, which aims to accelerate the convergence speed, while t1 is diminishing when the population is switching to the exploration state, in which more attentions are put on the diversity maintenance. On the other hand, regarding the evaluating number for original optimization (i.e., t2), which shows an opposite trend to t1, it is small during the exploitation stage but gradually increases later. In this way, a dynamic trade-off between convergence and diversity is achieved in S-WOF. Secondly, to further improve the search ability in the large-scale decision space, an efficient competitive swarm optimizer (CSO) is implemented in S-WOF, which shows efficiency for solving LSMOPs. Finally, the experimental results have validated the superiority of S-WOF over several state-of-the-art large-scale evolutionary algorithms.

Spectral features and optimal Hierarchical attention networks for pulmonary abnormality detection from the respiratory sound signals

The most important concern in the medical field is to consider the analysis of data and perform accurate diagnosis. However, the analysis of pulmonary abnormalities may depend on the diagnostic experience and the medical skills of the physicians, and is a time-consuming practice. In order to solve such issues, an efficient Water Cycle Swarm Optimizer-based Hierarchical Attention Network (WCSO-based HAN) is developed for detecting the pulmonary abnormalities from the respiratory sound signals. However, the developed optimization technique named WCSO is devised by incorporating the Water Cycle Algorithm (WCA) with Competitive Swarm Optimizer (CSO). Here, the pre-processing is performed using the Hanning window and Spectral gating-based noise reduction method in order to remove the falsifications or noises from the signal. Thereafter, the process of feature extraction is carried out to extract the significant features, such as Bark frequency Cepstral coefficient (BFCC) and the short-term features, such as spectral flux and spectral centroid. Once the significant features are extracted, classification is performed using HAN where the training procedure of HAN is carried out using WCSO. Furthermore, the developed WCSO-based HAN obtained efficient performance using True Positive Rate (TPR), True Negative Rate (TNR) and accuracy with the values of 0.943, 0.913, and 0.923 using dataset 1, respectively. All test results are validated with the k-fold cross validation method in order to make an assessment of the generalizability of these results.

Automated Test Case Generation Based on Competitive Swarm Optimizer with Schema and Node Branch Archive

Automated Test Case Generation Based on Competitive Swarm Optimizer with Schema and Node Branch Archive

A Cluster-Based Competitive Particle Swarm Optimizer with a Sparse Truncation Operator for Multi-Objective Optimization

Many different types of multi-objective optimization problems, e.g. multi-modal problems and large-scale problems, have been solved with high performance by numbers of tailored multi-objective evolutionary algorithms. Little attention has been paid on sparse optimization problems, whose most decision variables are zero in the Pareto optimal solution set. Most recently, algorithms for solving sparse problems have been developed rapidly, and many sparse optimization problems in machine learning, such as the search for lightweight neural networks, can be solved with the help of multi-objective evolutionary algorithms. In this paper, we introduce a sparse truncation operator which uses the accumulative gradient value as a criterion for setting a decision variable to zero. In addition, to balance the exploration and exploitation, a cluster-based competitive particle swarm optimizer is proposed, which takes advantage of both particle swarm optimization and competitive swarm optimizer to search efficiently and escape from local optima. Consequently, aiming at solving sparse multi-objective optimization problems, a novel cluster-based competitive particle swarm optimizer with a sparse truncation operator is proposed, and experimental results show that the proposed algorithm outperforms its peers on sparse test instances and neural network training tasks.

Optimal Deep Transfer Learning-Based Human-Centric Biomedical Diagnosis for Acute Lymphoblastic Leukemia Detection.

Human-centric biomedical diagnosis (HCBD) becomes a hot research topic in the healthcare sector, which assists physicians in the disease diagnosis and decision-making process. Leukemia is a pathology that affects younger people and adults, instigating early death and a number of other symptoms. Computer-aided detection models are found to be useful for reducing the probability of recommending unsuitable treatments and helping physicians in the disease detection process. Besides, the rapid development of deep learning (DL) models assists in the detection and classification of medical-imaging-related problems. Since the training of DL models necessitates massive datasets, transfer learning models can be employed for image feature extraction. In this view, this study develops an optimal deep transfer learning-based human-centric biomedical diagnosis model for acute lymphoblastic detection (ODLHBD-ALLD). The presented ODLHBD-ALLD model mainly intends to detect and classify acute lymphoblastic leukemia using blood smear images. To accomplish this, the ODLHBD-ALLD model involves the Gabor filtering (GF) technique as a noise removal step. In addition, it makes use of a modified fuzzy c-means (MFCM) based segmentation approach for segmenting the images. Besides, the competitive swarm optimization (CSO) algorithm with the EfficientNetB0 model is utilized as a feature extractor. Lastly, the attention-based long-short term memory (ABiLSTM) model is employed for the proper identification of class labels. For investigating the enhanced performance of the ODLHBD-ALLD approach, a wide range of simulations were executed on open access dataset. The comparative analysis reported the betterment of the ODLHBD-ALLD model over the other existing approaches.

Cumulative learning-based competitive swarm optimizer for large-scale optimization

Cumulative learning-based competitive swarm optimizer for large-scale optimization

Enhancing extreme learning machines classification with moth-flame optimization technique

Extreme Learning Machine (ELM) algorithm assigns the input weights and biases in a “one-time stamp” fashion, this method makes the algorithm to be ill-conditioned and reduces its classification accuracy. The contribution of this work is the enhancement of the performance of ELM with the Moth-Flame Optimization (MFO) algorithm to improve classification accuracy. A hybrid of the Moth-Flame Optimization and Extreme Learning Machine (MFO-ELM) algorithm is implemented in MATLAB. MFO ensures a concurrent simulation of exploration and exploitation of the search space to select an optimum candidate solution. The candidate solution is reshaped into input weights and biases for ELM classification. The hybrid algorithm is validated on five life-selected datasets. The performance improvement of MFO-ELM is compared with ELM-optimized Particle Swarm Optimization (PSO-ELM) and Competitive Swarm Optimization (CSO-ELM) algorithms. The improvement rates are qualitatively and quantitatively evaluated to show the improvement of MFO-ELM on ELM and the other meta-heuristic algorithms. MFO-ELM improved the accuracies of the basic ELM in all 100% of the simulations and performed better than the other meta-heuristic algorithms in 80% of the simulations. The performance of MFO-ELM is more competitive, and it is recommended for solving classification problems.

Optimal cluster head selection for energy efficient wireless sensor network using hybrid competitive swarm optimization and harmony search algorithm

A Wireless Sensor Network (WSN) is a network made up of many sensor nodes that are geographically dispersed. Because the sensor nodes in WSN are battery-powered, energy efficiency is a major issue. As a result, it is critical to make effective use of available energy in order to avoid energy waste. The clustering system provides an energy-efficient method of data transfer. The existing clustering scheme utilises distinctive Low-Energy Adaptive Clustering Hierarchy (LEACH) and optimization approaches faces issues like complicated local search, trade off in exploration and exploitation. A combination of the Harmony Search Algorithm (HSA) and the Competitive Swarm Optimization (CSO) is developed for energy efficient selection of cluster head (CH) to provide a global search with quick convergence rate. The proposed method has a high HSA search efficiency and a dynamic CSO capability that extends the life of sensor nodes. The proportion of alive nodes, count of dead nodes, residual energy and throughput are used to measure the hybrid algorithm’s performance. In terms of throughput and residual energy, the suggested hybrid HSA-CSO algorithm surpasses the current methods.

A competitive swarm optimizer with probabilistic criteria for many-objective optimization problems

Although multiobjective particle swarm optimizers (MOPSOs) have performed well on multiobjective optimization problems (MOPs) in recent years, there are still several noticeable challenges. For example, the traditional particle swarm optimizers are incapable of correctly discriminating between the personal and global best particles in MOPs, possibly leading to the MOPSOs lacking sufficient selection pressure toward the true Pareto front (PF). In addition, some particles will be far from the PF after updating, this may lead to invalid search and weaken the convergence efficiency. To address the abovementioned issues, we propose a competitive swarm optimizer with probabilistic criteria for many-objective optimization problems (MaOPs). First, we exploit a probability estimation method to select the leaders via the probability space, which ensures the search direction to be correct. Second, we design a novel competition mechanism that uses winner pool instead of the global and personal best particles to guide the entire population toward the true PF. Third, we construct an environment selection scheme with the mixed probability criterion to maintain population diversity. Finally, we present a swarm update strategy to ensure that the next generation particles are valid and the invalid search is avoided. We employ various benchmark problems with 3–15 objectives to conduct a comprehensive comparison between the presented method and several state-of-the-art approaches. The comparison results demonstrate that the proposed method performs well in terms of searching efficiency and population diversity, and especially shows promising potential for large-scale multiobjective optimization problems.

Bat competitive swarm optimizer enabled DMN for automatic recommendation of learning objects

Bat competitive swarm optimizer enabled DMN for automatic recommendation of learning objects

An effective chronic lymphocytic leukemia detection method using hybrid optimization aware random multimodal deep learning

AbstractThe leukemia represents a blood cancer that begins from bone marrow and spread to bloodstream and other limbs. The leukocytes pose various morphologies and size that makes it complex to detect and segment exactly. To consider this issue, this article develops a novel and efficient method for chronic lymphocytic leukemia (CLL) detection with blood smear images. Here, the preprocessing is done with Type 2 fuzzy and cuckoo search‐based filter for discarding artifacts and noise contained in the images. The cell segmentation is done with deep fuzzy clustering for producing the segments from the preprocessed blood smear images. The discovery of CLL is done with random multimodal deep learning (RMDL). The RMDL training is done with proposed Jaya‐competitive swarm optimization (Jaya‐CSO) algorithm. The developed Jaya‐CSO is devised by combining Jaya optimization and competitive swarm optimizer (CSO). Hence, the proposed Jaya‐CSO‐based RMDL is employed for discovering lymphocytic leukemia. The proposed Jaya‐CSO‐based RMDL offered improved performance with highest accuracy of 95.1%, highest true negative rate of 95.4% and highest true positive rate of 95.2%.

CPRO: Competitive Poor and Rich Optimizer-Enabled Deep Learning Model and Holoentropy Weighted-Power K-Means Clustering for Brain Tumor Classification Using MRI

A brain tumor is a collection of irregular and needless cell development in the brain region, and it is considered a life-threatening disease. Therefore, early level segmentation and brain tumor detection with Magnetic Resonance Imaging (MRI) is more important to save the patient’s life. Moreover, MRI is more effective in identifying patients with brain tumors since the recognition of this modality is moderately larger than considering other imaging modalities. The classification of brain tumors is the most important, difficult task in medical imaging systems because of size, appearance and shape variations. In this paper, Competitive Poor and Rich Optimization (CPRO)-based Deep Quantum Neural Network (Deep QNN) is proposed for brain tumor classification. Additionally, the pre-processing process assists in eradicating noises and uses image intensity to eliminate the artifacts. The significant features are extracted from pre-processed image to perform a productive classification process. The Deep QNN classifier is employed for classifying the brain tumor regions. Besides, the Deep QNN classifier is trained by the developed CPRO approach, which is newly designed by integrating Poor and Rich Optimization (PRO) and Competitive Swarm Optimizer (CSO). The developed brain tumor detection model outperformed other existing models with accuracy, sensitivity and specificity of 94.44%, 97.60% and 93.78%.

X-ray image based COVID-19 detection using evolutionary deep learning approach

Radiological methodologies, such as chest x-rays and CT, are widely employed to help diagnose and monitor COVID-19 disease. COVID-19 displays certain radiological patterns easily detectable by X-rays of the chest. Therefore, radiologists can investigate these patterns for detecting coronavirus disease. However, this task is time-consuming and needs lots of trial and error. One of the main solutions to resolve this issue is to apply intelligent techniques such as deep learning (DL) models to automatically analyze the chest X-rays. Nevertheless, fine-tuning of architecture and hyperparameters of DL models is a complex and time-consuming procedure. In this paper, we propose an effective method to detect COVID-19 disease by applying convolutional neural network (CNN) to the chest X-ray images. To improve the accuracy of the proposed method, the last Softmax CNN layer is replaced with a K-nearest neighbors (KNN) classifier which takes into account the agreement of the neighborhood labeling. Moreover, we develop a novel evolutionary algorithm by improving the basic version of competitive swarm optimizer. To this end, three powerful evolutionary operators: Cauchy Mutation (CM), Evolutionary Boundary Constraint Handling (EBCH), and tent chaotic map are incorporated into the search process of the proposed evolutionary algorithm to speed up its convergence and make an excellent balance between exploration and exploitation phases. Then, the proposed evolutionary algorithm is used to automatically achieve the optimal values of CNN’s hyperparameters leading to a significant improvement in the classification accuracy of the proposed method. Comprehensive comparative results reveal that compared with current models in the literature, the proposed method performs significantly more efficient.

Learning Competitive Swarm Optimization.

Particle swarm optimization (PSO) is a popular method widely used in solving different optimization problems. Unfortunately, in the case of complex multidimensional problems, PSO encounters some troubles associated with the excessive loss of population diversity and exploration ability. This leads to a deterioration in the effectiveness of the method and premature convergence. In order to prevent these inconveniences, in this paper, a learning competitive swarm optimization algorithm (LCSO) based on the particle swarm optimization method and the competition mechanism is proposed. In the first phase of LCSO, the swarm is divided into sub-swarms, each of which can work in parallel. In each sub-swarm, particles participate in the tournament. The participants of the tournament update their knowledge by learning from their competitors. In the second phase, information is exchanged between sub-swarms. The new algorithm was examined on a set of test functions. To evaluate the effectiveness of the proposed LCSO, the test results were compared with those achieved through the competitive swarm optimizer (CSO), comprehensive particle swarm optimizer (CLPSO), PSO, fully informed particle swarm (FIPS), covariance matrix adaptation evolution strategy (CMA-ES) and heterogeneous comprehensive learning particle swarm optimization (HCLPSO). The experimental results indicate that the proposed approach enhances the entropy of the particle swarm and improves the search process. Moreover, the LCSO algorithm is statistically and significantly more efficient than the other tested methods.

Firefly Competitive Swarm Optimization Based Hierarchical Attention Network for Lung Cancer Detection

Lung cancer is a severe disease, which causes high deaths in the world. Earlier discovery of lung cancer is useful to enhance the rate of survival in patients. Computed Tomography (CT) is utilized for determining the tumor and identifying the cancer level in the body. However, the issues of CT images cause less tumor visibility areas and unconstructive rates in tumor regions. This paper devises an optimization-driven technique for classifying lung cancer. The CT image is utilized for determining the position of the tumor. Here, the CT image undergoes segmentation, which is performed using the DeepJoint model. Furthermore, the feature extraction is carried out, wherein features such as local ternary pattern-based features, Histogram of Gradients (HoG) features, and statistical features, like variance, mean, kurtosis, energy, entropy, and skewness. The categorization of lung cancer is performed using Hierarchical Attention Network (HAN). The training of HAN is carried out using proposed Firefly Competitive Swarm Optimization (FCSO), which is devised by combining firefly algorithm (FA), and Competitive Swarm Optimization (CSO). The proposed FCSO-based HAN provided effective performance with high accuracy of 91.3%, sensitivity of 88%, and specificity of 89.1%.

Prognosis of Electric Scooter With Intermittent Faults: Dual Degradation Processes Approach

Prognosis of failing components under intermittent faults is challenging since intermittent faults gradually deteriorate in duration and magnitude over time meanwhile show the stochasticity of fault appearance and disappearance. To address the problem, this paper proposes an intelligent prognosis method for intermittently faulty components in electric scooter based on dual degradation processes. Firstly, fault detection and isolation is used to identify discrete faults and isolate possible intermittent faults in continuous components, where the fault isolation performance under multiple faults condition is improved by developing an extended fault signature matrix. Secondly, the adaptive competitive swarm optimization is proposed to identify the magnitude, appearing and disappearing instants of each intermittent fault for faulty components. After that, the dual degradation processes are established with the aid of tumbling window (TW), where the duration degradation process describes the evolutionary trend of the ratio of fault duration to the length of duration-TW, while the magnitude degradation process captures the evolutionary trend of maximum feature in magnitude-TW. With dual degradation processes and predefined failure thresholds, the remaining useful life of the faulty component is jointly predicted, where the degradation speed difference between duration and magnitude is considered. Finally, the proposed methods are validated by experiment results.

Economical planning of fuel cell vehicle-to-grid integrated green buildings with a new hybrid optimization algorithm

Popularity of fuel cell electric vehicles (FCVs) is an important criterion for solving the global problem of reducing CO2 emissions. However, the overall cost of FCVs and hydrogen fuel production is relatively high, so FCV promotion is slow. Considering that FCVs have near-zero CO2 emissions and high endurance, which is suitable for vehicle-to-grid (V2G) systems, this study aims to analyze the economic potential of the fuel cell vehicle-to-grid (FCV2G) systems to promote FCVs to the highest level. For this purpose, a large-scale green building was first selected as the research target and an agent to provide V2G services for the power grid. Then, Monte Carlo method was used to simulate the vehicle visiting time. A discharge model was also developed. Considering CO2 emission price and self-elasticity coefficient of discharge price, an overall economic optimization model was presented. Then, the hybrid algorithm of competitive swarm optimization (CSO) and imperialist competitive algorithm (ICA) was applied to optimize the model, which not only led to definite results and reduced standard deviation, but also eliminated the weakness of the CSO, i.e., convergence speed and poor performance in some benchmark functions. The simulation results indicated the proposed algorithm had faster convergence and more accuracy in finding the optimal solution than other optimization algorithms. Moreover, the overall economic profit improved in the presence of FCVs. Finally, sensitivity analysis was performed on six parameters, including daily electricity price, battery cost, fuel cell cost, CO2 emission price, power grid carbon emission, and hydrogen cost. The results showed FCV2G system had high development potential as well as great economic profit increasing over time.