As a high-speed, noninvasive process measurement technology, electrical resistance tomography (ERT) is well suited for visualization of media distribution in industrial and biomedical fields. An ...innovative optimal hybrid deep-learning strategy utilizing a 1-D convolution neural network (CNN) and recurrent neural network (RNN) is proposed to solve the ERT inverse problem. In the proposed hybrid deep-learning model, the priority-based adaptive firefly algorithm (PAFA) optimizes the neuron structure by feature engineering and adaptively estimates the local optimum to accelerate convergence. The feature selection technique controls randomness through efficient local search and finding the optimal value fit. The input and output relation is enforced by local recurrent cells in the hybrid model feedback with a bidirectional long-short-term memory (BiLSTM) network. The proposed model extracts the latent features from the prior cells during the training period of the network architecture. Simulation and experimental tests are carried out, and the quantitative analysis shows that the proposed hybrid deep-learning model has better imaging accuracy than the traditional image reconstruction methods.
The problem of distribution is a problem that cannot be separated from the industrial world. These problems are because these problems have a considerable influence on the cost and level of service ...to consumers. Therefore we need a way so that the distribution process can run smoothly and on time. One way that can be done in the distribution process is to optimize vehicle routes so that the time spent serving two consumers is more efficient and the goods can get to consumers on time. The problem of optimizing vehicle routes is known as vehicle routing problem (VRP). VRP which aims to form an optimal route to serve consumers with capacity constraints and Type of Vehicle is called Multi-type vehicle routing problem (MTVRP). In solving the MTVRP problem, two methods will be combined namely the exact method and the heuristic method. The firefly algorithm has been widely used in solving VRP problems. The results are obtained total distance of 2488 km.
Photovoltaic (PV) string exhibits complex multiple-peak characteristics under various partial shading conditions (PSC). If the maximum power point tracking cannot be achieved quickly and accurately, ...it will lead to a large amount of energy loss. Therefore, it has become a hot topic to study a reliable maximum power tracking control algorithm to ensure the PV system can still output maximum power under PSC. This paper proposes an immune firefly algorithm (IFA), which utilizes vaccine data-base to shorten the convergence time, eliminates the influence of bad individuals in time by immune replenishment operation, and reduces the steady-state oscillation by the improving iteration formula. The simulations in static and dynamic environments verify that the immune firefly algorithm can track the maximum power point under various partial shading conditions. Compared with conventional firefly algorithm (FA), IFA has faster convergence speed, and can effectively restrain the oscillation of voltage and power.
The electrochemical proton exchange membrane fuel cell (PEMFC) is an electrical generator that utilizes a chemical reaction mechanism to produce electricity, serving as a sustainable and ...environmentally friendly energy source. To thoroughly analyze and develop the features and performance of a PEMFC, it is essential to use a precise model that incorporates exact parameters to effectively suit the polarization curve. In addition, parameter extraction plays a crucial role in the simulation analysis, evaluation, optimum control, and fault detection of the proton exchange membrane fuel cell (PEMFC) system. Despite the development of many algorithms for parameter extraction in PEMFC, obtaining accurate and trustworthy results rapidly remains a challenge. This study presents a hybridized algorithm, namely differential evolution ameliorated (DEA) for reliably estimating PEMFC model parameters. To evaluate the proposed DEA-based parameter identification, a comparison analysis with previously published methods is conducted using MATLAB/SimulinkTM (R2016b, MathWorks, Natick, MA, USA) in terms of system correctness and convergence process. The proposed DEA algorithm is tested to extract the parameters of two PEMFC models: SR-12 500 W and 250 W. The sum of the squared errors (SSE) between the experimental and the obtained voltage data is defined as an objective function. The simulation results prove that the suggested DEA algorithm is capable of identifying the optimal PEMFC parameters rapidly and accurately in comparison with other optimization algorithms.
As the world population and its dependency on energy is growing exponentially day by day, the existing energy generating resources are not enough to fulfill their needs. In the conventional grid ...system, most of the generated energy is wasted because of improper demand side management (DSM). This leads to a difficulty in keeping the equilibrium between the user need and electric power production. To overcome these difficulties, smart grid (SG) is introduced, which is composed of the integration of two-way communication between the user and utility. To utilize the existing energy resources in a better way, SG is the best option since a large portion of the generated energy is consumed by the educational institutes. Such institutes also need un-interrupted power supply at the lowest cost. Therefore, in this paper, we have taken a university campus load. We have not only applied two bio-inspired heuristic algorithms for energy scheduling—namely, the Firefly Algorithm (FA) and the Lion Algorithm (LA)—but also proposed a hybrid version, FLA, for more optimal results. Our main objectives are a reduction in both, that is, the cost of energy and the waiting time of consumers or end users. For this purpose, in our proposed model, we have divided all appliances into two categories—shiftable appliances and non-shiftable appliances. Shiftable appliances are feasible to be used in any of the time slots and can be planned according to the day-ahead pricing signal (DAP), provided by the utility, while non-shiftable appliances can be used for a specified duration and cannot be planned with the respective DAP signal. So, we have scheduled shiftable appliances only. We have also used renewable energy sources (RES) for achieving maximum end user benefits. The simulation results show that our proposed hybrid algorithm, FLA, has reduced the cost excellently. We have also taken into consideration the consumers’ waiting times, due to scheduling of appliances.
In this paper, novel 3D node localization algorithms using applications of Computational Intelligence (CI) for moving target nodes are attained using single reference node (anchor node) in an ...anisotropic network. Target nodes are randomly deployed at beneath and middle layers. Whereas single anchor node is deployed at top layer. Recent applications of Computational Intelligence (CI), i.e., Particle Swarm Optimization (PSO), H-Best Particle Swarm Optimization (HPSO), Biogeography Based Optimization (BBO) and Firefly Algorithm (FA) are used respectively to estimate the optimum location of moving target nodes. In this paper, each target node has heterogeneous properties (due to different battery backup statuses). Degree of Irregularity (DOI) of 0.01 is considered for radiation pattern. In heterogeneous network, the geometric distance between two nodes is not proportional to their hop count. Once a moving target falls under the range of a deployed anchor node, three virtual anchor nodes (minimum 4 anchor nodes are required for 3D positions) in surrounding of anchor and respective moving target node are projected by using umbrella projection form to find the 3D position. The proposed algorithms are designed for rescue operations in highly hostile environment.
Defects occurring in software product are a universal event. Prevention of these defects in the early stage needs more attention because early stage prevention and fixing requires less effort and ...lower cost. Software defect prediction (SDP) is necessary in the determination of software quality as well as reliability. Prediction of defects is relatively an original research area in software quality engineering. Coverage of key predictors and the kind of data to be collected along with defect prediction model role, the interdependence of defects and predictors can be recognized in software quality. Feature selection (FS) is one of the worthy preprocessing techniques for application that uses huge volumes of data. It is the process of selecting the probable minimal attribute which is expected to be represented in the set of actual attributes. This paper proposes, FS using firefly algorithm (FA) and classifiers like support vector machine (SVM), Naïve Bayes (NB) as well as K-nearest neighbor (KNN) are used for classifying the features selected. The FS that make use of the FA is that new technique of evolutionary computation that has been inspired by the process of flash lighting of the fireflies. This can search quickly the feature space for an optimal or a near optimal feature subset for minimizing a certain function of fitness. This proposed fitness function has made use of the incorporation of both the accuracy of classification and the reduction of the size. The results of the experiment have shown that the FS using the FA can achieve a better accuracy of classification than that of the other methods.
In this paper, we consider a power splitting (PS)-based simultaneous wireless information and power transfer (SWIPT) system, which is having I and Q (in-phase and quadrature) imbalance hardware ...impairment. The prime objective of this paper is to maximize the harvested energy for a SWIPT PS system, satisfying a minimum signal to noise ratio (SNR) requirement for information signal processing in the presence of IQ imbalance hardware impairment. The paper uses four types of bio-inspired algorithms like Particle Swarm Optimisation, Artificial Bee Colony, Firefly Algorithm and Invasive Weed Optimisation to attain the maximum harvested energy considering parameters of PS ratio, amplitude and phase imbalances in a Rayleigh fading environment. The simulation results show that under ideal power conversion efficiency and minimum hardware impairments, a maximum SNR of 16.7 dB and harvested energy of 11 dB is achieved for a transmit power of 20 dB in SWIPT systems for IWO bio-inspired algorithms in the 32 QAM modulation scheme.
•FA tuned controller with online wavelet filter is presented.•Time delay, dead zone, boiler, GRC and noise are incorporated into the AGC model.•Signal integrity index is formulated to evaluate filter ...performance.•Online wavelet filter outperforms conventional low pass filter.•Robustness and efficiency of proposed tuning method and wavelet filter is shown.
This paper presents the implementation of the Firefly Algorithm (FA) with an online wavelet filter on the automatic generation control (AGC) model for a three unequal area interconnected reheat thermal power system. The model includes time delay, dead zone, boiler, Generation Rate Constraint (GRC), and high frequency noise components. A novel filtering technique based on wavelet transform is introduced for the purpose of removing noise(s) from the ACE signal. The performance of the filter is measured by formulating a signal integrity index. The simulation results show that the FA is able to outperform the Particle Swarm Optimization (PSO) in obtaining the minimum objective function based on Integral of Time Weighted Squared Error (ITSE). The results also shows that the proposed online wavelet filter performs with a higher degree of efficiency compared to the conventional low pass filter when the practical model of the AGC is analyzed. Further investigation by varying the GRC and time delay parameter confirms the robustness of the FA tuned controller with the online wavelet filter.
The optimization of energy systems within a multi-microgrid framework, enriched by shared Battery Energy Storage Systems (BESS), has emerged as a compelling avenue for enhancing the efficiency of ...distributed energy networks. In response to the increasing integration of BESS in modern energy systems, this study investigates the implications of incorporating BESS within connected residential-commercial Microgrids (MGs). Unlike previous studies that primarily focused on cost and reliability, this research fills a significant gap in the literature by investigating the optimization of load demands patterns. Specifically, we explore the impact of shared BESS on load demand patterns in commercial-residential MGs. The research introduces two innovative critical load metrics, peak-to-average ratio (PAR) and demand profile smoothness (DPS), to assess the influence of BESS on demand profiles. In addition, the study explores the integration of a Dynamic Thermal Rating (DTR) system, compared to traditional fixed thermal rating systems, to further optimize the performance and efficiency of connected residential-commercial MGs enriched by shared BESS. Three distinct case studies, each comprising a commercial MG (shopping mall, hotel, and office building) paired with a residential MG, were considered. Utilizing a Firefly Algorithm (FA) for optimization, the study determines the optimized BESS capacity for minimum total cost. The results highlight that the implementation of shared BESS, especially in collaboration between commercial and residential MGs, significantly reduces imported energy from the main grid, enhancing MG flexibility and resilience. While the economic benefits of shared BESS may not be substantial (up to 3.25 % cost reduction), the study underscores its contribution to more balanced and smoother load demand curves, with improvements in PAR (up to 15.63 %) and DPS (up to26.05 %). Moreover, considering DTR for transmission lines, instead of fixed thermal rating, improves the PAR and DPS up to 28.52 % and 41.06 % respectively. The findings of this study highlight the importance of considering load demand patterns in the design and operation of MGs and underscore the multifaceted benefits of shared BESS beyond economic considerations.
•The results highlight that the implementation of shared BESS, especially in collaboration between commercial and residential MGs, significantly reduces imported energy.•Shared BESS leads to more balanced and smoother load demand curves, with improvements in PAR (up to 15.63%) and DPS (up to 26.05%).•Advantages of considering DTR for transmission lines, improving PAR and DPS by up to 28.52% and 41.06% respectively.
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