Firefly Particle Research Articles

Fine-tuned Siamese neural network–based multimodal vein biometric system with hybrid firefly–particle swarm optimization

Fine-tuned Siamese neural network–based multimodal vein biometric system with hybrid firefly–particle swarm optimization

Enhancing Intercultural Business English Communication Factors Evaluation System Using the Termite Life Cycle Optimization Algorithm and Dynamically Stabilized Recurrent Neural Network

In today's globalized business environment, effective intercultural communication in English is paramount for successful collaboration among professionals from diverse backgrounds. To enhance the accuracy of the evaluation system, enhancing intercultural business English communication factors evaluation system using the termite life cycle optimization algorithm and dynamically stabilized recurrent neural network (IBEC–DSRNN–TLCOA) is proposed in this manuscript. The input image is captured from mobile camera. Then the input images are preprocessed using intelligent weight decreasing firefly–particle filtering (IWDFPF) to remove noise and enhance the input images. Afterwards, the preprocessed image is fed to the entropy-founded spatial fuzzy C-means clustering approach for segmenting the image. Then the contrast, correlation, energy and homogeneousness features are extracted by using force-invariant improved feature extraction technique. The extracted features are given to dynamically stabilized recurrent neural network (DSRNN) to image target detection and English description generation. Termite life cycle optimization algorithm (TLCOA) is employed to enhance the weight parameters of DSRNN. The proposed IBEC–DSRNN–TLCOA method is implemented. The proposed IBEC–DSRNN–TLCOA method provides 32.53%, 31.86%, and 35.72% higher accuracy; 35.58%, 32.16%, and 37.72% higher F-measure when compared with the existing methods, such as exploration of intelligent translation with evaluation systems for business English (IBEC–RCNN), E-learning engagement with convolution neural networks on business education (IBEC–CNN), and deep neural network-based research on scoring business English oral training (IBEC–DNN), respectively.

Comparison of a Hybrid Firefly–Particle Swarm Optimization Algorithm with Six Hybrid Firefly–Differential Evolution Algorithms and an Effective Cost-Saving Allocation Method for Ridesharing Recommendation Systems

The optimization and allocation of transport cost savings among stakeholders are two important issues that influence the satisfaction of information providers, drivers and passengers in ridesharing recommendation systems. For optimization issues, finding optimal solutions for nonconvex constrained discrete ridesharing optimization problems poses a challenge due to computational complexity. For the allocation of transport cost savings issues, the development of an effective method to allocate cost savings in ridesharing recommendation systems is an urgent need to improve the acceptability of ridesharing. The hybridization of different metaheuristic approaches has demonstrated its advantages in tackling the complexity of optimization problems. The principle of the hybridization of metaheuristic approaches is similar to a marriage of two people with the goal of having a happy ending. However, the effectiveness of hybrid metaheuristic algorithms is unknown a priori and depends on the problem to be solved. This is similar to a situation where no one knows whether a marriage will have a happy ending a priori. Whether the hybridization of the Firefly Algorithm (FA) with Particle Swarm Optimization (PSO) or Differential Evolution (DE) can work effectively in solving ridesharing optimization problems needs further study. Motivated by deficiencies in existing studies, this paper focuses on the effectiveness of hybrid metaheuristic algorithms for solving ridesharing problems based on the hybridization of FA with PSO or the hybridization of FA with DE. Another focus of this paper is to propose and study the effectiveness of a new method to allocate ridesharing cost savings to the stakeholders in ridesharing systems. The developed hybrid metaheuristic algorithms and the allocation method have been compared with examples of several application scenarios to illustrate their effectiveness. The results indicate that hybridizing FA with PSO creates a more efficient algorithm, whereas hybridizing FA with DE does not lead to a more efficient algorithm for the ridesharing recommendation problem. An interesting finding of this study is very similar to what happens in the real world: “Not all marriages have happy endings”.

A hybrid firefly particle swarm for low-temperature dairy items assignment considering shelf display space profit disparities and two-phase demand

A hybrid firefly particle swarm for low-temperature dairy items assignment considering shelf display space profit disparities and two-phase demand

Hybrid firefly particle swarm optimisation algorithm for feature selection problems

AbstractFeature selection techniques play a vital role in the processes that deal with enormous amounts of data. These techniques have become extremely crucial and necessary for data mining and machine learning problems. Researchers have always been in a race to develop and provide libraries and frameworks to standardise this procedure. In this work, we propose a hybrid meta‐heuristic algorithm to facilitate the problem of feature selection for classification problems in machine learning. It is a python based, lucid and efficient algorithm geared towards optimising and striking a balance between the number of features selected and accuracy. The proposed work is a binary hybrid of existing meta‐heuristic algorithms, the particle swarm optimisation (PSO) algorithm, and the firefly algorithm (FA) such that it blends the best of each algorithm to provide an optimised and efficient way of solving the said problem. The suggested approach is assessed against six datasets from different domains that are publicly available at the UCI repository to demonstrate its validity. The datasets are Breast cancer, Iris, WBC, Mushroom, Glass ID, and Abalone. This approach has also been evaluated against similar, such evolutionary‐based approaches to prove its superiority. Various metrics such as accuracy, precision, recall, f1 score, number of selected features, and run time have been analysed, measured, and compared. The hybrid firefly particle swarm optimisation algorithm is found to be suitable for feature selection problems.

Voltage stability and power loss minimization with STATCOM using particle swarm optimization and hybrid firefly particle swarm optimization algorithms

This paper presents a hybrid algorithm using a hybrid firefly and particle swarm optimization (HFPSO) algorithm to determine the optimal placement of STATCOM devices. A multi-objective function is used to increase voltage stability, voltage profile and minimize overall power losses of the transmission system. At first, PSO algorithm is used to find the optimal STATCOM location and further more to reduce the power losses and voltage profile enhancement to overcome the sub-optimal operation of existing algorithms, the HFPSO algorithm is used to determine the optimal placement of STATCOM FACTS device and verification of the proposed algorithm was achieved on standard IEEE 14-bus and 30-bus transmission systems in the MATLAB environment. Comprehensive simulation results of two different cases show that the proposed HFPSO demonstrates significant improvements over other existing algorithms to enhance voltage stability and reducing active and reactive power losses in an electrical power transmission systems.

A Novel Blind Image Source Separation Using Hybrid Firefly Particle Swarm Optimization Algorithm

Signal and image separation are extensively used in numerous imaging applications and communication systems. In this paper, a novel Blind Source Separation (BSS) approach, based on the Hybrid Firefly Particle Swarm Optimization (HFPSO), is proposed for separating mixed images. This approach processes the observed source without any prior knowledge about the model and the statistics of the source signal. The proposed method presents high robustness against local minima and converges quickly to the global minimum. Via numerical simulations, the proposed approach is tested and validated in comparison with standard Particle Swarm Optimization (PSO), Robust Independent Component Analysis (RobustICA), and Artificial Bee Colony (ABC) algorithms. The obtained results show that the presented technique outperforms the existing ones in terms of quality of image separation, the Signal-to-Noise Ratio (PSNR), and Structural Similarity Index Measure (SSIM). Moreover, the obtained results demonstrate that our approach provides also promising results in image separation from noisy mixtures.

Automatic Data Clustering by Hybrid Enhanced Firefly and Particle Swarm Optimization Algorithms

Data clustering is a process of arranging similar data in different groups based on certain characteristics and properties, and each group is considered as a cluster. In the last decades, several nature-inspired optimization algorithms proved to be efficient for several computing problems. Firefly algorithm is one of the nature-inspired metaheuristic optimization algorithms regarded as an optimization tool for many optimization issues in many different areas such as clustering. To overcome the issues of velocity, the firefly algorithm can be integrated with the popular particle swarm optimization algorithm. In this paper, two modified firefly algorithms, namely the crazy firefly algorithm and variable step size firefly algorithm, are hybridized individually with a standard particle swarm optimization algorithm and applied in the domain of clustering. The results obtained by the two planned hybrid algorithms have been compared with the existing hybridized firefly particle swarm optimization algorithm utilizing ten UCI Machine Learning Repository datasets and eight Shape sets for performance evaluation. In addition to this, two clustering validity measures, Compact-separated and David–Bouldin, have been used for analyzing the efficiency of these algorithms. The experimental results show that the two proposed hybrid algorithms outperform the existing hybrid firefly particle swarm optimization algorithm.

Clinical Efficacy of Interventional Chemotherapy Embolization Combined with Monopolar Radiofrequency Ablation on Patients with Liver Cancer.

Due to the greater prevalence of chronic hepatitis B infection, liver tumor is especially popular in China. In China, it is the 4th most prevalent tumor and the 3rd main reason for cancer fatalities. Hepatocellular carcinomas (HCCs) account for more than 91% of every liver tumor case, and chemotherapy and immunotherapy are the better therapy choices. It is a serious threat to the lives and health of Chinese citizens. Patients diagnosed with liver tumors have a bad prognosis. Surgical resection, liver transplantation, chemotherapeutic embolization, and radiofrequency ablation (RFA) are all choices for patients who are detected early. More effective therapies can result in a better prognosis. This paper analyzes the clinical efficiency of interventional transarterial chemoembolization (TACE) integrated with monopolar radiofrequency ablation (RFA) on patients with a liver tumor. Initially, the dataset is collected and the patients are treated with combined TACE and RFA. The computed tomography (CT) images are obtained using three-phase CT imaging. The images are segmented using adaptive U-Net-based segmentation. The clinical efficiency of the patients is evaluated using Robust Residual Convolutional Neural Network (RR-CNN) which is optimized using Firefly Particle Swarm Optimization (FPSO) algorithm. The performance of the system is analyzed using the MATLAB simulation tool. In performance analysis, the proposed method of RR-CNN is high when compared to the existing method of CNN, logistic regression using genetic algorithm and KNN in overall parameters are accuracy, sensitivity, F1-score, and specificity. These integrated treatments have a suggested greater response frequency, indicating a synergistic impact by combination treatment in the initial stages.

Day-Ahead DSM-Integrated Hybrid-Power-Management-Incorporated CEED of Solar Thermal/Wind/Wave/BESS System Using HFPSO

This paper presents a day-ahead demand-side management (DSM)-integrated hybrid power management algorithm (PMA) with an objective of combined economic and emission load dispatch (CEED) considering losses. The algorithm was tested on an IEEE 30-bus six-generator system consisting of solar thermal/wind/wave/battery energy storage systems (BESSs) considering real-time data of the Gujarat (19°07′ N, 72°51′ E) coastal region and diverse renewable energy (RES) and storage sources. A maiden attempt of utilizing hybrid firefly particle swarm optimization (HFPSO) to reduce thermal energy consumption and carbon emission was presented. Further, a novel attempt for a versatile renewable power management system was proposed based on a day-ahead pricing scheme to manage load demand and generation effectively. The PMA permits the users to bring down the general load demand and adjust the load curve during the peak time frame. The comparative performance of particle swarm optimization (PSO), firefly algorithm (FA), and HFPSO algorithms in solving the objective was presented. The HFPSO algorithm was found to be the best in terms of a fuel cost of 544.160 (USD/h), emission 20.301 (kg/h), and peak-load reduction of 31.292%, 24.210%, and 51.197% for residential, commercial, and industrial loads, respectively, when contrasted with the other two algorithms PSO and FA.

A novel intelligent weight decreasing firefly–particle filtering method for accurate state‐of‐charge estimation of lithium‐ion batteries

A novel intelligent weight decreasing firefly–particle filtering method for accurate state‐of‐charge estimation of lithium‐ion batteries

Application of HFPSO-TOPSIS approach for optimally locating and sizing of reactive power compensating devices for voltage control ancillary service

Modern power system with renewables in distribution network has made the optimal sizing and location of reactive power support crucial and essential. By optimal locating and sizing of reactive power support resources causes a power loss reduction, improvement in voltage profile and maximizes techno-economic benefits to consumers and system operators while improving overall system performance and reliability. However, the optimal location of the reactive power supporting device (OLRPSD) and its sizing for voltage control ancillary service is a multi-objective problem requiring a multi-objective multi-criteria decision-making (MOMCDM) approach. In literature, objectives of reactive power supporting have been to minimize the power losses. But it has more than one objective that depends upon the reactive power supporting device. Moreover, the economic benefit from reactive power support also requires to be considered, which is of utmost importance for all stakeholders. In this paper, OLRPSD is performed considering financial benefit by an objective of reactive power cost minimization along with other targets like power loss reduction, maximization stability margin of voltage, and minimization of deviation of voltage with an application of a recently developed MOMCDM technique known as Hybrid Firefly Particle Swarm Optimization with TOPSIS approach (HFPSO-TOPSIS) and is therefore new. This is executed on “modified IEEE 33 bus” radial distribution network. Various reactive power compensating devices considered are Distributed generation (DG’s), Batteries, capacitors and D-STATCOM and Electric vehicle charging stations. The results show the merit of this method over the existing ones.

The impact of local search strategies on chaotic hybrid firefly particle swarm optimization algorithm in flow-shop scheduling

A flow-shop production environment is a widely used production setting in the industry, especially preferred when the variety of products and processes is limited. The constant development of technology and the increasing competition in the market compel researchers to seek ways to increase efficiency. Therefore, scheduling remains an important area of study for researchers. When the size of the problem increases, particularly in real-life contexts, it becomes almost impossible to work out a feasible solution to scheduling problems through current computer technologies. In such cases, solutions are often developed using heuristic or meta-heuristic methods. However, the performance of these methods may vary according to the problem or the components of the suggested method of solution.This study aimed to investigate the effect of local search strategies on the solution performance of hybrid firefly and particle swarm optimization algorithm, developed using chaotic local search. We used four different solution methods: the triple local search strategy consisting of insertion, swap and inversion, which were used frequently in earlier studies, and the individual use of each of these three strategies. In this study, the average relative difference values calculated using insertion, swap, inversion and triple strategy were found to be 3.03, 1.27, 2.54 and 1.20, respectively. As a result of the analyses carried out to compare the results of the solutions obtained in the flow-shop scheduling problems of Taillard, it was found that better results were obtained using the triple strategy. These findings can be a point of reference for future research.

Using chaos enhanced hybrid firefly particle swarm optimization algorithm for solving continuous optimization problems

Optimization becomes more important and the use of optimization methods is becoming widespread with the developments in computer sciences. Researchers from different scientific fields are looking for better solutions to solve complex problems with optimization methods. In some complex problems, optimal results can be obtained utilizing metaheuristic algorithms. Researchers carry out different studies to improve the performance of present metaheuristic algorithms. Although the success of metaheuristic algorithms has been seen in previous studies, there are some weaknesses in these algorithms. Therefore, successful results cannot be obtained for each problem sometimes. In order to overcome this problem, more successful algorithms can be obtained by hybridizing the strong points of the different methods together. In addition, one of the important factors affecting the success of optimization algorithms is scanning ability of the solution space in order to find the optima. Exploring search space is carried out using random variables by some metaheuristic algorithms. The chaotic values that are generated by chaotic maps can be used instead of random values. Thus, search ability of algorithms performs more dynamically. In this study, hybrid firefly and particle swarm optimization algorithms are transformed to a chaotic-based algorithm by use of 10 different chaotic maps. Random valued parameters are generated by chaotic maps. In order to indicate the performances between different dimensions, CEC 2015 benchmark and constraint problems are used in experimental studies. Chaos enhanced methods are compared against canonical and hybrid optimization algorithms. It has been seen that obtained results of the proposed method were sufficiently successful and reliable.

Probabilistic routing protocol with firefly particle swarm optimisation for delay tolerant networks enhanced with chaos theory

In this paper, we propose a proactive routing paradigm for delay tolerant networks (DTN) using probabilistic routing implemented via firefly particle swarm optimisation and optimised by chaos maps. DTN is a specific class of networks where there is absence of an end to end connectivity among nodes and are characterised by long or variable delays, asymmetric data rates and high error rates. The routing scheme considers the constrained environment of DTN nodes and implements a meta-heuristic paradigm to facilitate data transfer. The chaos maps further enhance the probabilistic routing parameters controlling brightness and ageing by randomising them. The protocol is implemented on ONE simulator and its performance is compared with traditional DTN routing schemes across delivery probability, average latency, overhead ratio and buffer utilisation. The simulation results show that our proposed protocol performs better than its contemporary ant PRoPHET protocol across the various performance metrics.

Probabilistic routing protocol with firefly particle swarm optimisation for delay tolerant networks enhanced with chaos theory

In this paper, we propose a proactive routing paradigm for delay tolerant networks (DTN) using probabilistic routing implemented via firefly particle swarm optimisation and optimised by chaos maps....

EMG SIGNAL DENOISING USING ADAPTIVE FILTERS THROUGH HYBRID OPTIMIZATION ALGORITHMS

Electromyography (EMG) signal recording equipment is comparatively modern. Still, there are enough restrictions in detection, recording, and characterization of EMG signals because of nonlinearity in the equipment, which leads to noise components. The most commonly affecting artifacts are Power Line Interference (PLI-Noise), Baseline Wander noise (BW-Noise), and Electrocardiogram noise (ECG-Noise). Adaptive filters are advanced and effective solutions for EMG signal denoising, but the improper tuning of filter coefficients leads to noise components in the denoised EMG signal. This defect in adaptive filters triggers or motivates us to optimize the filter coefficients with existing meta-heuristics optimization algorithms. In this paper, Least Mean Squares (LMS) filter and Recursive Least Squares (RLS) adaptive filter coefficients are optimized with a new Hybrid Firefly–Particle Swarm Optimization (HFPSO) by taking the advantages and disadvantages of both the algorithms. Experiments are conducted with the proposed HFPSO and it proved better in EMG signal denoising in terms of the measured parameters like signal-to-noise ratio (SNR) in dB, maximum error (ME), mean square error (MSE), etc. In the second part of the work, the denoised EMG signal features are extracted for the diagnosis of diseases related to myopathy and neuropathy as EMG signal reflects the neuromuscular function and EMG signal examination may contribute to the diagnosis of muscle disorder linked to myopathy and neuropathy.

A New Hybrid Firefly–PSO Optimized Random Subspace Tree Intelligence for Torrential Rainfall-Induced Flash Flood Susceptible Mapping

Flash flood is one of the most dangerous natural phenomena because of its high magnitudes and sudden occurrence, resulting in huge damages for people and properties. Our work aims to propose a state-of-the-art model for susceptibility mapping of the flash flood using the decision tree random subspace ensemble optimized by hybrid firefly–particle swarm optimization (HFPS), namely the HFPS-RSTree model. In this work, we used data from a flood inventory map consisting of 1866 polygons derived from Sentinel-1 C-band synthetic aperture radar (SAR) data and a field survey conducted in the northwest mountainous area of the Van Ban district, Lao Cai Province in Vietnam. A total of eleven flooding conditioning factors (soil type, geology, rainfall, river density, elevation, slope, aspect, topographic wetness index (TWI), normalized difference vegetation index (NDVI), plant curvature, and profile curvature) were used as explanatory variables. These indicators were compiled from a geological and mineral resources map, soil type map, and topographic map, ALOS PALSAR DEM 30 m, and Landsat-8 imagery. The HFPS-RSTree model was trained and verified using the inventory map and the eleven conditioning variables and then compared with four machine learning algorithms, i.e., the support vector machine (SVM), the random forests (RF), the C4.5 decision trees (C4.5 DT), and the logistic model trees (LMT) models. We employed a range of statistical standard metrics to assess the predictive performance of the proposed model. The results show that the HFPS-RSTree model had the best predictive performance and achieved better results than those of other benchmarks with the ability to predict flash flood, reaching an overall accuracy of over 90%. It can be concluded that the proposed approach provides new insights into flash flood prediction in mountainous regions.

Optimal power flow using hybrid firefly and particle swarm optimization algorithm.

In this paper, a novel, effective meta-heuristic, population-based Hybrid Firefly Particle Swarm Optimization (HFPSO) algorithm is applied to solve different non-linear and convex optimal power flow (OPF) problems. The HFPSO algorithm is a hybridization of the Firefly Optimization (FFO) and the Particle Swarm Optimization (PSO) technique, to enhance the exploration, exploitation strategies, and to speed up the convergence rate. In this work, five objective functions of OPF problems are studied to prove the strength of the proposed method: total generation cost minimization, voltage profile improvement, voltage stability enhancement, the transmission lines active power loss reductions, and the transmission lines reactive power loss reductions. The particular fitness function is chosen as a single objective based on control parameters. The proposed HFPSO technique is coded using MATLAB software and its effectiveness is tested on the standard IEEE 30-bus test system. The obtained results of the proposed algorithm are compared to simulated results of the original Particle Swarm Optimization (PSO) method and the present state-of-the-art optimization techniques. The comparison of optimum solutions reveals that the recommended method can generate optimum, feasible, global solutions with fast convergence and can also deal with the challenges and complexities of various OPF problems.

Boosted Relief Feature Subset Selection and Heterogeneous Cross Project Defect Prediction using Firefly Particle Swarm Optimization

The exponential growth in the field of information technology, need for quality-based software development is highly demanded. The important factor to be focused during the software development is software defect detection in earlier stages. Failure to detect hidden faults will affect the effectiveness and quality of the software usage and its maintenance. In traditional software defect prediction models, projects with same metrics are involved in prediction process. In recent years, active topic is dealing with Cross Project Defect Prediction (CPDP) to predict defects on software project from other software projects dataset. Still, traditional cross project defect prediction approaches also require common metrics among the dataset of two projects for constructing the defect prediction techniques. Suppose if cross project dataset with different metrics has to be used for defect prediction then these methods become infeasible. To overcome the issues in software defect prediction using Heterogeneous cross projects dataset, this paper introduced a Boosted Relief Feature Subset Selection (BRFSS) to handle the two different projects with Heterogeneous feature sets. BRFSS employs the mapping approach to embed the data from two different domains into a comparable feature space with a lower dimension. Based on the similarity measure the difference among the mapped domains of dataset are used for prediction process. This work used five different software groups with six different datasets to perform heterogeneous cross project defect prediction using firefly particle swarm optimization. To produce optimal defect prediction in the Heterogeneous environment, the knowledge of particle swarm optimization by inducing firefly algorithm. The simulation result is compared with other standard models, the outcome of the result proved the efficiency of the prediction process while using firefly enabled particle swarm optimization.