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Wire electrical discharge machining (WEDM) is a highly regarded machining approach for its exceptional accuracy and adaptability, there is a need to further enhance its performance by optimizing critical process parameters. The objective is to examine the optimized setting of WEDM parameters with a focus on improving key performance metrics such as material removal rate (MRR), surface roughness (SR), and taper angle (TA). The desired responses are greatly influenced by critical process parameters, including pulse ON Time (50–60 μs), pulse OFF Time (25–34 μs), gap voltage (25–250 V), peak current (1–6 A), and dielectric flow rate (1–3 LPM). This study investigates the use of novel bio-inspired optimization algorithms, namely the African vulture optimization algorithm (AVOA), Firefly algorithm (FA), and cuckoo search algorithm (CSA), for optimizing process parameters in WEDM. The results are compared with the RSM approach. It was found that a significant reduction in MRR of 57.97%, an increase in TA of 6.21%, followed by enhancement in the surface finish of 22.98% attained with AVOA. The optimal parameters obtained with AVOA are TON of 51 μs, TOFF of 30 μs, peak current of 1 A, gap voltage of 116 V, and dielectric flow rate of 3 LPM. The integration of the RSM with bio-inspired algorithms in the manufacturing domain can be implemented in the manufacturing industries. [ABSTRACT FROM AUTHOR]

Parameters are an important part of any metaheuristic algorithm. They play a pivotal role in deciding the results obtained from these algorithms. The problem of parameter tuning has become an optimization problem in itself, termed meta optimization. In the proposed work, a methodology for parameter tuning is proposed, in which the values of parameters vary randomly over an interval; hence, it is called random parameter tuning. It starts with dividing the whole population into sub-populations (called batches), and each batch is assigned a different range of parameter values. During each iteration, the value of a parameter is decided randomly according to the predefined interval of the corresponding batch. This approach is easy to understand and is general. It can be embedded with any of the metaheuristic algorithms. The proposed work has been embedded in the Genetic Algorithm, Particle Swarm Optimization, Firefly Algorithm, and Differential Evolution Algorithm. The approach has been tested over 15 benchmark functions, shifted rotated functions, and classical engineering problems. Moreover, the significance of the proposed approach is established by conducting a sensitivity analysis, a non-parametric Friedman test, and a Wilcoxon Rank Test. The proposed approach has been compared with the two state-of-the-art methods. The results show the superiority of the proposed approach. [ABSTRACT FROM AUTHOR]

Nowadays, technology has shifted the way individuals access news from conventional media sources to social media platforms. The active engagement of people with social media platforms leads them to consume news without confirming its source or legitimacy. This facilitated the dissemination of more manipulated and false information in the form of rumors and fake news. Fake news can affect public opinion and create chaos and panic among the population. Thus, it is essential to employ an advanced methodology to identify fake news with high precision. This research work has proposed the concept of the quantum-inspired firefly algorithm with the ant miner plus algorithm (QFAMP) for more effective fake news detection. The proposed QFAMP algorithm utilizes the attributes of quantum computing (QC), the firefly algorithm (FA), and the ant miner plus algorithm (AMP). Here, the QFA approach ensures the effective exploitation of the firefly agents until the agents are able to search for the brighter firefly. Further, the AMP algorithm utilizes the best ants with higher pheromone concentrations for global exploration, which also avoids the premature convergence of the QFA agents. In addition, the AMP algorithm serves as an efficient data mining variant that is effective for the classification of fake news. The efficacy of the proposed QFAMP algorithm is evaluated for the dataset of FakeNewsNet, which is composed of two sub-categories: BuzzFeed and PolitiFact. The experimental evaluations indicate the effective performance of the proposed algorithm compared to the other techniques. [ABSTRACT FROM AUTHOR]

The digitalization and intelligence of nuclear power plants (NPPs) enable neural networks to predict transient parameters, aiding operators in emergency responses. This paper addresses the limitations of the back propagation (BP) neural network, which can lead to local optima and reduced accuracy in predicting transient parameters, by introducing the Firefly Algorithm (FA) to optimize the BP network, creating the FA–BP neural network. Using transient data from PCTRAN, a nuclear power plant accident simulation software, the study compares the predictive performance of the FA–BP network with conventional BP and LSTM neural networks during loss of coolant accident and steam generator tube rupture accidents. The results show that the FA–BP network reduces prediction errors and achieves similar accuracy with shorter prediction times compared to LSTM, due to its simpler structure. The FA's global optimization capabilities enhance the BP network's performance, making it more effective in predicting long-term post-accident parameters. The FA–BP neural network model offers a promising approach for improving the accuracy and speed of transient parameter predictions, contributing to enhanced safety and control in nuclear power plants during emergencies. [ABSTRACT FROM AUTHOR]

The end-milling procedure has been widely used for machining glass-fiber-reinforced polymer composite (GFRP) materials. A complex interaction of reinforcing glass fibers with each other as well as the matrix element during the end-milling process can result in high cutting force (CF), surface roughness (SR), and delamination factor (DF) because of the anisotropic nature of GFRP. To reduce the three responses (CF, SR, and DF) at the same time, the end-milling cutting parameters, i.e., rotating speed (n), feed speed (Vf), and axial depth of cut (d), must carefully be determined. In this study, the end-milling of GFRP composites was investigated by utilizing a full factorial design of trials with three distinct values of n, Vf, and d. Also, a mix of genetic algorithms (GA) and backpropagation neural networks (BPNN) was administered to forecast the responses and obtain the optimized end-milling parameters. The firefly algorithm (FA), GA, and the integration of GA and the simulated annealing algorithm (SAA) were used to discover the best combination of end-milling parameter levels to reduce the responses' total variance. Later, the combination of BPNN and GA-SAA capable of accurately predicting multi-response characteristics and significantly improving multi-response characteristics was obtained through analyzing the confirmation experiment. [ABSTRACT FROM AUTHOR]

In the rapidly increasing pharmaceutical sector, sales representatives are employed by pharmaceutical manufacturers and distributors to inform and educate physicians. To convince providers to prescribe the medications to their patients, these representatives rely on their product expertise and people's abilities to close deals. Instead of making direct sales, pharmaceutical sales representatives help medical professionals get the medications, treatments, and information they need to give their patients the best care possible. Furthermore, they inform the public about novel and occasionally life-saving treatments and share interesting medical developments. This study presents a hybrid methodology that integrates the benefits of local search and the firefly algorithm (FA) to determine the optimal planning for a sales representative. The objective is to maximize the rewards while adhering to certain constraints. The objective is to maximize the rewards while adhering to certain limits. Utilizing local search, the hybrid algorithm enhances firefly's global search behaviour and produces the best possible sales presentation planning. The experimental findings demonstrate the superior performance of the suggested algorithm compared to the FA and other literature methods in the sense of enhancing the convergence rate and preventing local minima. Furthermore, it can enhance the best-known solution for most benchmark instances. [ABSTRACT FROM AUTHOR]

Data are typically collected from sensors distributed across the network and transmitted for analysis and processing to a central base station (BS). However, a significant challenge in Internet of Things (IoT) sensor networks is the efficient aggregation of data from multiple sensors to increase network longevity and reduce the consumption of energy. During the aggregation of data, sensor nodes often transmit redundant data due to multiple factors, including overlapping distribution. The network should gather redundant packets and convert them into aggregated data. Aggregation is necessary to remove duplicate data and convert it into unified data, a task that requires large amounts of energy. In this research paper, we suggest a technique for aggregating data in IoT sensor networks, using clustering with an optimized firefly algorithm (FA), taking into consideration both energy consumed and distance. In this approach, a particular number of nodes are identified in each round. These nodes have a proximate node with a distance less than the threshold. After that cluster heads (CHs) are elected strategically based on brighter fireflies (nodes with higher fitness). The FA is employed for this purpose, where fireflies represent the sensor nodes, and their attractiveness is determined by their fitness, representing the quality of their solutions. The simulation outcomes, executed in MATLAB 2023b, indicated that the suggested method, the firefly optimization algorithm (FOA), outperformed the FA and LEACH in improving the quality‐of‐service parameters. Furthermore, the ANOVA testing of the simulation result demonstrated the superiority of the proposed approach as well. [ABSTRACT FROM AUTHOR]

This study emphasizes the significance of stacking sequence and hybridization of glass, carbon, kevlar and basalt fibers to enhance the mechanical characteristics and the overall wear response of polymer composites. The carbon layer on the outside of the composite exhibited higher ultimate tensile and flexural strengths. The abrasive wear of fabricated hybrid composites is also explored by performing experiments using Box–Behnken design approach. The pin‐on‐disc tester is utilized to do the wear test by varying composite type, sliding distance, and sliding velocity, with specific wear rate (SWR) serving as the response parameter. Regression analysis is performed to predict SWR using control and response parameters derived from experimentation. A novel firefly algorithm technique is adopted to determine the optimal process parameter combination. By utilizing optimized parameters (430 m, 10.5 m/s, and the CKBG4BKC stacking sequence), the SWR is considerably reduced to 16.82 × 10−5 mm3/Nm. Scanning electron microscopy on the worn‐out wear surface reveals enhanced interfacial bonding, fiber breakage and plowing as the fundamental wear mechanism. This work provides insight into hybrid composites for constructing aircraft and automobile body structures, where they provide an optimal blend of strength, sustainability, and structural performance. Highlights: Hybrid composite: Stacking sequence impacts on mechanical and abrasive wear.Box–Behnken design: Applied on stacking order, sliding distance and velocity.Utilizing metaheuristic firefly algorithm to enhance specific wear results.Optimal parameters: 430 m, 10.5 m/s, and CKBG4BKC stacking sequence.Lightweight, high‐strength, cost‐effective, and sustainable hybrid composites. [ABSTRACT FROM AUTHOR]

The management of water resources and the modeling of river flow have a prominent position within environmental research. They form a critical bridge between human societies and the delicate ecosystems they inhabit. Scholars have focused on benefiting more efficient methods based on the use of artificial intelligence for river flow forecasting, notably because modeling hydrological systems is quite challenging. This study primarily centered on exploring the predictive capacities of hybrid models in establishing a link between daily flow data and prospective data. In the study, the mentioned algorithms, firefly algorithm (FFA), artificial bee colony (ABC), genetic algorithm (GA), were hybridized with the artificial neural network (ANN) model and data analyzes were examined with the stations in the Konya Closed Basin. A comparative analysis of FFA–ANN, GA–ANN, ABC–ANN, and long short-term memory (LSTM) models was conducted for daily flow forecasting for daily flow forecasting according to a range of graphical and statistical metrics. The outcomes indicate that the FFA–ANN hybrid model generally performed better than other models and the deep learning algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we develop an integral reinforcement learning (IRL)‐based trajectory tracking control (TTC) scheme via firefly optimized neural networks for partially unknown multiplayer nonlinear systems. Under the developed TTC scheme, IRL is proved to be equivalent to the classical policy iteration, which guarantees the convergence of the IRL algorithm. By implementing the IRL method, the requirement of the drift dynamics is obviated. The TTC policy for each player is obtained by solving the coupled Hamilton–Jacobi equation with a critic neural network, whose weight vector is tuned by the firefly algorithm. The tracking error of the closed‐loop system is guaranteed to be stable via the Lyapunov's direct method. Simulation results illustrate the effectiveness and superiority of the present IRL‐based TTC scheme, and show that the success rate of system operation is increased by introducing the firefly algorithm. [ABSTRACT FROM AUTHOR]

The escalation of Internet of Things (IoT) devices has led to increased data generation at the network edge that has burdened the cloud infrastructure in terms of handling and processing of data. This has led to the rapid adoption of fog computing because of its ability to bring computation and storage closer to the edge and support for real-time applications and services by reducing latency. One of the foremost challenges in the fog computing arena is minimizing turnaround time. This research paper proposes a Modified Levy Flight Firefly Algorithm (MLFFA) to optimize task scheduling for fog computing environments. Specifically, the objective is to minimize the turnaround time of tasks. Moreover, genetic operators like crossover and mutation are also employed to achieve an optimal balance between exploration and exploitation. Experimental observations undertaken show that the proposed method improves the average turnaround time by 55%, 22%, and 13%, average waiting time by 59%, 45%, and 37%, average energy consumption by 19%, 7%, and 4%, and average failure rate by 50%, 28%, and 7% compared to the existing studies, namely Load Balancing and Optimization Strategy (LBOS), Technique for Resource Allocation and Management (TRAM), and Fuzzy Golden Eagle Load Balancing (FGELB), respectively. [ABSTRACT FROM AUTHOR]

Partial shading is one of the crucial bottlenecks in solar photovoltaic (PV) system. The performance of a PV system is affected due to partial shading. This paper highlights the impact of partial shading condition (PSC) on the performance of PV systems with an experimental analysis using a PV emulator. A reduction of 37% in maximum power, 38% in fill factor, and 60% in efficiency as a result of PSC was observed in the experimentation work. PSC also results into multiple peaks on power-voltage (P-V) curve. One of these peaks is the Global Maximum Power Point (GMPP) and other peaks are local MPPs. The GMPP cannot be tracked using conventional MPPT algorithms. This paper proposes a new optimization method called as Firefly Algorithm (FA) built on a metaheuristic approach for Maximum Power Point Tracking (MPPT). Results obtained through the simulation show the enhancement in the tracking efficiency and tracking time over the conventional MPPT methods by achieving the tracking efficiency of 98.12% with a response time of less than 1ms. The proposed system is also able to reduce the oscillations around MPP and achieve stable performance under dynamically varying environmental conditions. [ABSTRACT FROM AUTHOR]

Abstract Facial expression recognition (FER) has advanced applications in various disciplines, including computer vision, Internet of Things, and artificial intelligence, supporting diverse domains such as medical escort services, learning analysis, fatigue detection, and human-computer interaction. The accuracy of these systems is of utmost concern and depends on effective feature selection, which directly impacts their ability to accurately detect facial expressions across various poses. This research proposes a new hybrid approach called QIFABC (Hybrid Quantum-Inspired Firefly and Artificial Bee Colony Algorithm), which combines the Quantum-Inspired Firefly Algorithm (QIFA) with the Artificial Bee Colony (ABC) method to enhance feature selection for a multi-pose facial expression recognition system. The proposed algorithm uses the attributes of both the QIFA and ABC algorithms to enhance search space exploration, thereby improving the robustness of features in FER. The firefly agents initially move toward the brightest firefly until identified, then search transition to the ABC algorithm, targeting positions with the highest nectar quality. In order to evaluate the efficacy of the proposed QIFABC algorithm, feature selection is also conducted using QIFA, FA, and ABC algorithms. The evaluated features are utilized for classifying face expressions by utilizing the deep neural network model, ResNet-50. The presented FER system has been tested using multi-pose facial expression benchmark datasets, including RaF (Radboud Faces) and KDEF (Karolinska Directed Emotional Faces). Experimental results show that the proposed QIFABC with ResNet50 method achieves an accuracy of 98.93%, 94.11%, and 91.79% for front, diagonal, and profile poses on the RaF dataset, respectively, and 98.47%, 93.88%, and 91.58% on the KDEF dataset.

Aiming at the problems of premature convergence and insufficient diversity of multi-objective firefly algorithm, this paper proposes a role division approach for firefly algorithm based on t-distribution perturbation and differential mutation. The idea of role division in nature is integrated into the firefly algorithm, and different roles are assigned to fireflies with different performances by the role division index, and the best learning mode is assigned according to the different roles, so as to realize the diversified learning of the population. The t-distribution perturbation with different degrees of freedom parameters is used instead of the original random perturbation, which can dynamically adjust the development and exploration ability of the algorithm in different periods. To avoid the algorithm falling into local optimality due to individual convergence at a later stage, differential mutation of the global optimal solution is performed to reduce the probability of the algorithm falling into stagnation and to balance convergence and diversity of the population. MOFA-PD is compared with 5 classical and 12 recent multi-objective optimization algorithms on 18 test functions, and the experimental results show that MOFA-PD has better advantages in convergence and diversity. [ABSTRACT FROM AUTHOR]

Abstract The Water Quality Index (WQI) is widely used as a classification indicator and essential parameter for water resources management projects. WQI combines several physical and chemical parameters into a single metric to measure the status of Water Quality. This study explores the application of five soft computing techniques, including Gene Expression Programming, Gaussian Process, Reduced Error Pruning Tree (REPt), Artificial Neural Network with FireFly (ANN-FFA), and combinations of Reduced Error Pruning Tree with bagging. These models aim to predict the WQI of Khorramabad, Biranshahr, and Alashtar sub-watersheds in Lorestan province, Iran. The dataset consists of 124 observations, with input variables being sulfate (SO4), total dissolved solids (TDS), the potential of Hydrogen (pH), chloride (Cl), electrical conductivity (EC), Potassium (K), bicarbonate (HCO), magnesium (Mg), sodium (Na), and calcium (Ca), and WQI as the output variable. For model creation (train subset) and model validation (test subset), the data were split into two subsets (train and test) in a ratio of 70:30. The performance evaluation parameters values of training and testing stages of various models indicate that the ANN-FFA based data-driven model performs better than the other modeling techniques applied with the values of coefficient of correlation 0.9990 & 0.9989; coefficient of determination 0.9612 & 0.9980; root mean square error 0.3036 & 0.3340; Nash–Sutcliffe error 0.9980 & 0.9979; and Mean average percentage error 0.7259% & 0.7969% for the train and test subsets, respectively. Taylor diagram results also suggest that ANN-FFA is the best-performing model, followed by the GEP model. This study introduces a novel model for predicting WQI using advanced soft computing models that have not been previously applied in this study area, highlighting its novelty and relevance. The proposed model significantly enhances predictive accuracy and efficiency, offering real-time, cost-effective WQI predictions that outperform traditional methods in handling complex, nonlinear environmental data.

The unbalanced load distribution in the electrical distribution network caused crucial power losses. This condition occurs in one of the electrical distribution networks, 20 kV Tarahan Substation, Province of Bandar Lampung, Indonesia. This condition can be maintained using optimal reconfiguration with the integration of Distributed Generation (DG) based on Renewable Energy (RE). This study demonstrates the optimal reconfiguration of the 20 kV Tarahan Substation with the integration of the Photovoltaic (PV) and Battery Energy Storage System (BESS). The reconfiguration process is optimized by using the Firefly Algorithm (FA). This process is conducted in the 24-hour simulation with various load profiles. The optimal reconfiguration is investigated in two scenarios based on without and with DG integration. The optimal configuration with more balanced load distribution conducted by FA reduces the power losses by up to 31.39% and 32.38% in without and with DG integration, respectively. Besides that, the DG integration improves the lowest voltage bus in the electrical distribution network from 0.95 p.u to 0.97 p.u.

The design and development of efficient microwave-absorbing and electromagnetic interference (EMI) shielding materials and structures to conceal electromagnetic (EM) waves remains a consistent and challenging task. Despite advancements in materials science and microwave engineering, there is a need for optimized materials that offer both effective microwave absorption and EMI shielding while minimizing material layer thickness. This research aims to address this gap by utilizing the firefly algorithm (FFA) to predict the optimal medium properties and thickness of microwave-absorbing and EMI shielding materials under specific constraints. In this context, a comprehensive investigation was carried out at the X-band involving numerical and experimental EM characterization of novel lightweight fiber-based samples. Additionally, the FFA has been applied to optimize these fiber-based microwave structures within the given constraints. Two separate objective functions (OBF) targeting minimum sample thickness, maximum microwave absorption, and shielding effectiveness (SE) bandwidth have been integrated into the FFA to address the thickness–bandwidth trade-off issue. Subsequently, resistive ink-coated glass fiber (IGF) and ink-coated mesh fiber (IMF) were developed and characterized based on the optimal solutions provided by the FFA. Consequently, an optimized IMF sample provides a minimum reflection coefficient (RC) of −19.0 dB at 10.7 GHz with a bandwidth of 2.8 GHz (9.6 to 12.4 GHz) below the −10 dB threshold. Besides, the optimal IGF sample achieves maximum SE of 11 dB at thickness of only 0.8 mm and covers the entire operating band. Furthermore, the response of the proposed structure was assessed for various oblique angles of incidence, revealing significant potential for various practical applications. A strong correlation between measured and theoretical findings underscores the potential of the proposed approach in realizing efficient microwave stealth and EMI shielding materials. [ABSTRACT FROM AUTHOR]

Engineering and economics both make extensive use of fractional programming. Because they are highly nonconvex and multimodal, they are regarded as challenging. This paper proposes an enhanced firefly algorithm (HFA) for solving fractional programming . The new population mean center is predicted by using the historical data of the population mean centers and added to the movement equation of fireflies to better guide their search. Numerical experiments are provided to demonstrate the efficiency and robustness of HFA. The results obtained by HFA show that it is always better than those produced by other methods. [ABSTRACT FROM AUTHOR]

The impact of drought builds on all three fronts of economy, environment, and society is devastating. Predicting its arrival and duration is highly important to arrange any sort of mitigation plans. The association of detailed relationship between multiple variables makes drought prediction a highly complex task. Especially influence of global warming, polar sea extent variations and their influence on overall ocean temperature have altered the seasonal rainfall behaviors all over the world. In the midst of it, predictions centered on the history of rainfall levels become inaccurate. The proposed system is an optimized deep learning prediction model integrating indigenous knowledge (IK) is proposed to predict the drought. IK expressed in human language is translated using fuzzy function and fed to an improved Long Short Term Memory (LSTM) model. The LSTM model hyperparameters are optimized using a hybrid of Particle Swarm Optimization (PSO) with firefly to produce the meta-heuristics algorithm which will provide the best performance in presence of integration of IK features into modern meteorological features which solves the problem of local minima in LSTM hyperparameter optimization. The performance of the proposed results were tested compared with the meteorological information gathered by the Karnataka Natural Disaster Monitoring Centre (KNDMC) for the district named Chitradurga of the Karnataka state in India. The proposed system which is Indigenous Knowledge merged along the cross model attention network can produce at least 1.4% higher Nash-Sutcliffe model efficiency coefficient (NSE) and 30% lower Mean Absolute Error (MAE) in the prediction of Standard Precipitation Index (SPI) compared to Convolution Neural Networks (CNN) and LSTM based time series prediction models. [ABSTRACT FROM AUTHOR]

Long-time discontinuous contact is easy to cause tool wear during milling. To decrease the impact of severe wear on workpiece quality and processing efficiency, cutting tools should be replaced timely. Therefore, tool wear prediction is an important aspect in improving process efficiency, ensuring machining precision and realizing intelligent manufacturing. To boost the precision of online prediction of tool wear, this paper suggests a novel approach to monitor tool wear by optimizing backpropagation (BP) neural network via firefly algorithm (FA). Specifically, the progressive semi-soft threshold function is applied to the process of cutting force signal noise reduction, which reduces redundant signals and noise interference in the signal. Time-domain analysis, frequency-domain analysis, and wavelet packet decomposition are utilized, cutting force features are extracted, and Pearson correlation coefficient is used to sift out signal features that are highly connected to tool wear. The FA is used to improve the BP neural network's weights and thresholds. Through learning nonlinear mapping, relationship between tool flank wear and signal features is realized. A prediction model of tool wear of FA-BP is constructed. Milling experiments validate the prediction model in the milling process. The experimental outcomes confirm the precision and reliability of the method. In comparison to BP neural network, genetic algorithm optimized BP neural network and particle swarm optimization algorithm optimized BP neural network prediction method, it has a greater prediction accuracy and a stronger training impact, and has superior performance. The research results can give a theoretical foundation and technological assistance for predicting tool wear, which is crucial for improving workpiece quality, processing efficiency, and promoting intelligent development. [ABSTRACT FROM AUTHOR]

With the explosive growth of electricity consumption, the demand for electricity by electricity users is increasing. As a core component of power supply, the safe and stable operation of transmission lines plays an important role in the normal operation of the entire power system. However, traditional monitoring methods for transmission line operation status face challenges such as limited accuracy, lack of real-time feedback, and high operational costs. In this paper, the Firefly algorithm is used to monitor the running status of transmission lines. Through synchronous testing with the traditional particle swarm optimization algorithm, it is found that the average accuracy of the Firefly algorithm in voltage and current measurement is improved to 93.13% and 93.66% respectively, which is better than the traditional algorithm. Firefly algorithm shows high precision in various power equipment monitoring, the average monitoring accuracy is 95.62% and 93.06%, respectively, which proves that it has stronger performance in transmission line monitoring and can achieve more stringent monitoring requirements. Through the comparison experiment of the algorithm, it proved that the Firefly algorithm had a strong performance in the transmission line operation status monitoring, and could more accurately identify the transmission line fault, which provided a new idea and new method for the safe operation status monitoring of transmission lines. [ABSTRACT FROM AUTHOR]

Accurately estimating the remaining useful life (RUL) of batteries is crucial for optimizing maintenance, preventing failures, and enhancing reliability, thereby saving costs and resources. This study introduces a hybrid approach for estimating the RUL of a battery based on the firefly algorithm–neural network (FA–NN) model, in which the FA is employed as an optimizer to fine-tune the network weights and hidden layer biases in the NN. The performance of the FA–NN is comprehensively compared against two hybrid models, namely the harmony search algorithm (HSA)–NN and cultural algorithm (CA)–NN, as well as a single model, namely the autoregressive integrated moving average (ARIMA). The comparative analysis is based mean absolute error (MAE) and root mean squared error (RMSE). Findings reveal that the FA–NN outperforms the HSA–NN, CA–NN, and ARIMA in both employed metrics, demonstrating superior predictive capabilities for estimating the RUL of a battery. Specifically, the FA–NN achieved a MAE of 2.5371 and a RMSE of 2.9488 compared with the HSA–NN with a MAE of 22.0583 and RMSE of 34.5154, the CA–NN with a MAE of 9.1189 and RMSE of 22.4646, and the ARIMA with a MAE of 494.6275 and RMSE of 584.3098. Additionally, the FA–NN exhibits significantly smaller maximum errors at 34.3737 compared with the HSA–NN at 490.3125, the CA–NN at 827.0163, and the ARIMA at 1.16e + 03, further emphasizing its robust performance in minimizing prediction inaccuracies. This study offers important insights into battery health management, showing that the proposed method is a promising solution for precise RUL predictions. [ABSTRACT FROM AUTHOR]

The extreme learning machine (ELM) stands out as a contemporary neural network learning model designed for neural networks, specifically emphasizing those with a single hidden layer. This model has gained significant importance in recent years and is frequently employed in research projects due to its reputation as one of the swiftest and most robust methods. ELM is distinguished by its ability to obtain accurate results without the need for prolonged training, setting it apart from other classifiers. Additionally, its reduced reliance on human intervention significantly diminishes the likelihood of errors. Despite their considerable potential, ELMs are not extensively employed. One contributing factor could be the ongoing challenges that ELM is yet to overcome, requiring successful resolution. A prevalent issue is the model's performance being notably dependent on the weights, biases within the hidden layer, and the quantity of neurons in that layer. Optimizing the number of neurons, referred to as hyperparameter optimization, falls under the category of NP-hard optimization problems. The second challenge lies in training the ELM, which involves establishing the weights and biases tailored for a specific task, presenting another NP-hard challenge. The research presented in this manuscript concentrates on addressing both aspects: optimizing hyperparameters, specifically the number of neurons in the hidden layer, and training the network to fine-tune the weights and biases. The main goal of this research is to effectively resolve both optimization and training by utilizing an improved swarm intelligence algorithm. As a result, both issues were addressed using an adapted version of the firefly algorithm. The proposed approach was tested and validated on twelve authentic datasets and four synthetic datasets designed for classification purposes. One of the forefront tasks among them involves the fetal nonstress test, commonly known as the cardiotocography problem, requiring the interpretation of data from two wearable sensors to discriminate between 3 and 10 imbalanced classes. The obtained outcomes are compared with the results reached by similar state of the art approaches, and the simulations show that the firefly algorithm improved by the group search operator can lead to superior performance. Additionally, enhancements of proposed method are confirmed by rigid statistical tests and results of best generated model for significant heart disease dataset are interpreted by valuable Shapley Additive Explanations (SHAP) tool. [ABSTRACT FROM AUTHOR]

Bayesian network is currently one of the most effective theoretical models in the field of uncertain knowledge expression and inference. Before utilizing Bayesian networks for analysis and inference, it is first necessary to obtain their network models through structural and parametric learning, and structure learning is the basis for parameter learning. Aiming at the existing firefly algorithm that does not conform to biological rules as well as learning the Bayesian network structure that has low efficiency and is easy to fall into local optimization, MGM-FA (firefly algorithm based on mutual information and gender mechanism) was designed. Firstly, the Bayesian network skeleton graph was obtained by calculating the mutual information of nodes, and the MGM-FA algorithm was driven to generate the initial population based on the skeleton graph. Secondly, a personalized Bayesian network population updating strategy based on the gender mechanism was introduced to safeguard the diversity of the Bayesian network individuals. Lastly, the local optimizer and perturbation operator were introduced to enhance the algorithm's ability of optimality seeking. Simulation experiments were carried out on standard networks of different sizes respectively, and the accuracy and efficiency of the algorithm are improved compared with existing algorithms of the same type. [ABSTRACT FROM AUTHOR]

Firefly algorithm (FA) combined with extreme learning machine (ELM) is developed for spectral interval selection and quantitative analysis of complex samples. The method firstly segments the spectra into a certain number of intervals. Vectors with 1 and 0, which represent the interval selected or not, are used as the inputs of the FA. The RMSEP value predicted by ELM model is used as the fitness function of the FA. The activation function and number of hidden layer nodes of ELM, number of spectral intervals, population number, environmental absorbance, and constant of FA are optimized. The predictive performance of FA‐ELM is compared with full‐spectrum PLS, ELM, genetic algorithm‐ELM (GA‐ELM), and particle swarm optimization‐ELM (PSO‐ELM) by one ultraviolet (UV) spectrum dataset of gasoil and three near‐infrared (NIR) spectral datasets of corn, wheat, and tablet samples. The results show that FA‐ELM has a better performance compared with its competitors in predicting monoaromatics, water, wheat kernel texture, and active pharmaceutical ingredients (APIs) in gasoil, corn, wheat, and tablet samples. [ABSTRACT FROM AUTHOR]

It is known that manually detecting heart conditions is often costly and time-consuming and any study regarding diagnose these conditions has a great importance. In this study, a metaheuristic optimization model has been developed to automate the detection of heart diseases with artificial intelligence compatible methods. In the proposed model, the feature set is selected to represent the best heart sound signals and heart disease diagnoses using machine learning algorithms with these feature sets. The proposed method has been tested on the Pascal dataset which consists of four classes. Firstly, an electronic-based filter model is used as low-pass filter and has great potential to use as a filtering for heart sound signals to decrease noise. Secondly, the statistical and acoustic feature vector extracted from the audio signals in the Pascal dataset is passed through particle swarm optimization (PSO), firefly algorithm (FA) and cuckoo search algorithm (CSA), and the most suitable feature vector is selected. After obtaining the most suitable feature vector with metaheuristic optimization algorithms and filtering method, heart disease diagnosis is performed using random forest (RF), K-nearest neighbor (K-NN), support vector machine (SVM) and Naive Bayes machine learning algorithms. [ABSTRACT FROM AUTHOR]

The current research is based on the explanation of the optimal model for predicting the performance of companies using data mining techniques. The method of this research is of the applied type, in terms of the way of doing the work, it is of the descriptive-causal research type, and in terms of the time dimension, it is of the post-event research type. In the first step, by referring to databases such as thesis, articles and similar researches, the required literature was collected in order to write the theoretical foundations and background of the research. In the following, the information of the investigated companies selected as a statistical sample, whose information is available in the form of data banks on CDs and is under the supervision and review of the responsible institutions, was audited by referring to the financial statements and new implementation software was compiled. The mentioned information included the financial data of the companies admitted to the Tehran Stock Exchange for a period of 10 years from the beginning of 2011 to the end of 2014. Finally, the findings showed that the firefly algorithm, genetic algorithm and evolutionary algorithm were effective in predicting the ratio of QTobins, return on equity and return on equity, and the firefly algorithm had a higher power to predict the ratio of QTobins, return on equity and return. has shares. [ABSTRACT FROM AUTHOR]

This research aims to simultaneously use geochemical modeling and geological parameters for gold grade estimation to identify promising zones of epithermal gold mineralization in the Zailik region, northwest of Iran. For this purpose, the employed geological evidence includes lithology and alterations like silicification, iron oxides, phyllic, and propylitic. For geochemical modeling two methods were utulized: 1) artificial neural network (ANN), 2) integrating ANN with the Firefly algorithm. Geological evidence after quantification, along with the estimated amounts of gold in artificial intelligence methods, was entered into the hierarchical system in Expert Choice software for weighting. In this method, the weighting and determination of the degree of relative importance of geological parameters were attempted after consulting geological and exploration experts. Subsequently, artificial intelligence methods were also compared with each other using quantitative criteria such as the coefficient of determination and the root mean square error function. The results showed that the combined method of artificial neural networks with the Firefly algorithm provides better results due to the higher coefficient of determination (R2=0.643) and lower error function (RMSE=0.754). Therefore, it has a higher degree of importance to identify promising areas for mineralization. Finally, all the above parameters were combined with each other in the Arc GIS software using the fuzzy overlay method, and the optimal exploration targets were detected in the north and northeast of the region, enabling to continue the exploration targets along the root of gold mineralization in the neighboring areas according to the introduced model. [ABSTRACT FROM AUTHOR]

Vehicle routing problem (VRP) is a classic NP-hard optimization problem. It is generally accepted that an optimized routing scheme can cause huge difference in the cost in all stages of transportation. Consequently, the VRP has evoked interest among the researchers of the field. Usually, a metaheuristic or an evolutionary algorithm is used for the solution of a VRP variant. In the last years, a number of swarm intelligence algorithms have been used for the solution of the problem. Initially, the two most classic swarm intelligence algorithms, the Ant Colony Optimization and the Particle Swarm Optimization, were used for the solution of this kind of problems. However, in the last years, more and more researchers solved the problem using a different swarm intelligence algorithm. In this paper, we focused in the presentation and analysis of the swarm intelligence algorithms that have been used for the solution of the problem. We give the advantages and disadvantages of each method, we focus in those ones that produced the best results in difficult VRPs and we present directions for the future of this kind of algorithms for the solution of a VRP variant. [ABSTRACT FROM AUTHOR]

The unbalanced load distribution in the electrical distribution network caused crucial power losses. This condition occurs in one of the electrical distribution networks, 20 kV Tarahan Substation, Province of Bandar Lampung, Indonesia. This condition can be maintained using optimal reconfiguration with the integration of Distributed Generation (DG) based on Renewable Energy (RE). This study demonstrates the optimal reconfiguration of the 20 kV Tarahan Substation with the integration of the Photovoltaic (PV) and Battery Energy Storage System (BESS). The reconfiguration process is optimized by using the Firefly Algorithm (FA). This process is conducted in the 24-hour simulation with various load profiles. The optimal reconfiguration is investigated in two scenarios based on without and with DG integration. The optimal configuration with more balanced load distribution conducted by FA reduces the power losses by up to 31.39% and 32.38% in without and with DG integration, respectively. Besides that, the DG integration improves the lowest voltage bus in the electrical distribution network from 0.95 p.u to 0.97 p.u. [ABSTRACT FROM AUTHOR]

Microarray-based cancer biomarker detection is one of the popular trends for cancer classification. Though existing approaches have given competing performance in terms of classification accuracy and reduced feature subsets, the classification of different cancer microarray datasets still requires improvements. Recently, the swarm-based hybrid algorithms have given significant performance in cancer classification. However, the efficiency of a swarm algorithm is dominated by certain factors such as fitness value, convergence, exploration, and exploitation capabilities. Thus, a swarm-based hybrid approach is proposed for cancer classification with a new variant of the Firefly Algorithm (FA) and Correlation-based Feature Selection (CFS) filter. The slow convergence issue in the FA is resolved by non-fixed size solutions termed as mutable size solutions and a composite position update function is designed for the mutable solutions. In addition, the local optima issue is overcome by the population reinitialisation method. The proposed algorithm, named the CFS-Mutable Composite Firefly Algorithm (CFS-MCFA), is evaluated based on two metrics, namely classification accuracy and genes subset size, using a Support Vector Machine (SVM) classifier. Results show that CFS-MCFA-SVM achieved 100% accuracy with only a few biomarkers for all four cancer microarray datasets, indicating the efficiency and the competing performance of the proposed algorithm in biomarker detection for microarray-based cancer classification. Apart from that, the proposed algorithm would also contribute to cancer-related issues upon verifying the relevancy of particular genes via technical analysis from a medical perspective and would be utilised in feature selection applications.

This paper explores the use of the Firefly Algorithm (FA) and its binary variant (BFA) in optimizing analog circuit component sizing, specifically as a case study for a two-stage operational amplifier (op-amp) designed with a 65nm CMOS process. Recognizing the limitations of traditional optimization approaches in handling complex analog design requirements, this study implements both FA and BFA to enhance convergence speed and accuracy within multi-dimensional search spaces. The Python-Spectre framework in this paper facilitates automatic, iterative simulation and data collection, driving the optimization process. Through extensive benchmarking, the BFA outperformed traditional FA, balancing exploration and exploitation while achieving superior design outcomes across key parameters such as voltage gain, phase margin, and unity-gain bandwidth. Comparative analysis with existing optimization methods, including Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), underscores the efficiency and accuracy of BFA in optimizing circuit metrics, particularly in power-constrained environments. This study demonstrates the potential of swarm intelligence in advancing automatic analog design and establishes a foundation for future enhancements in analog circuit automation.

Abstract Load frequency control (LFC) plays a critical role in ensuring the reliable and stable operation of power plants and maintaining a quality power supply to consumers. In control engineering, an oscillatory behavior exhibited by a system in response to control actions is referred to as “Porpoising”. This article focused on investigating the causes of the porpoising phenomenon in the context of LFC. This paper introduces a novel methodology for enhancing the performance of load frequency controllers in power systems by employing rat swarm optimization (RSO) for tuning and detecting the porpoising feature to ensure stability. The study focuses on a single-area thermal power generating station (TPGS) subjected to a 1% load demand change, employing MATLAB simulations for analysis. The proposed RSO-based PID controller is compared against traditional methods such as the firefly algorithm (FFA) and Ziegler-Nichols (ZN) technique. Results indicate that the RSO-based PID controller exhibits superior performance, achieving zero frequency error, reduced negative peak overshoot, and faster settling time compared to other methods. Furthermore, the paper investigates the porpoising phenomenon in PID controllers, analyzing the location of poles in the s-plane, damping ratio, and control actions. The RSO-based PID controller demonstrates enhanced stability and resistance to porpoising, making it a promising solution for power system control. Future research will focus on real-time implementation and broader applications across different control systems.