Search Optimization Algorithm Research Articles

Application of Search Algorithms in Determining of Fault Location on Overhead Power Lines According to the Emergency Mode Parameters

Identification of fault locations (FL) on overhead power lines (OHPL) in the shortest possible time allows to reduce the time for shutting down OHPL in case of damage. This helps to improve the reliability of power systems. FL devices on OHPL according to the emergency mode parameters (EMP) are widely used, as they have a lower cost. However, they have a larger error than FL devices that record traveling wave processes. Most of the well-known algorithms for FL on OHPL by EMP assume a uniform distribution of resistivity along the OHPL. In real conditions, this is not the case. Application of these algorithms in FL devices on OHPL with inhomogeneities leads to significant errors in calculating the distance to the fault location. The authors proposed the use of search algorithms for u-nconstrained one-dimensional optimization to increase the speed of implementation of iterative procedures in FL devices on OHPL by EMP. The authors have developed recommendations for choosing optimization criteria, as well as options for implementing computational procedures. Using the example of a two-sided FL on OHPL, it is shown that the use of search algorithms can significantly (from tens to hundreds of times) reduce the number of steps of the computational iterative procedure. The implementation of search algorithms is possible in the software of typical relay protection and automation terminals, without their hardware upgrade.

Optimal intelligent fuzzy TID controller for an uncertain level process with actuator and system faults: Population-based metaheuristic approach

For the Conical Tow-Tank Non-Interacting Level (CTTNL) prototype system, this article discusses the design of an unique optimal fractional order TID controller employing a type-1 fuzzy set. To generate a linearized model transfer function for the nonlinear CTTNL process, local linearization around the equilibrium state was performed. This work compares the performance of a fuzzy tilted-integral-derivative (FTID) controller for the CTTNL system to a traditional proportional–integral–derivative (PID) controller. The parameters of the FTID controller were optimized using the Chicken Search Optimization (CSO) algorithm in this study, and those values were used to create a PID controller. Thereafter, the FTID and PID controllers’ performance is compared. The investigation shows that the FTID controller beats the PID controller in terms of IAE, ISE, ITAE, and ITSE integral errors. The suggested controller’s fault-tolerant performance was also assessed using the fault recovery time (Frt) parameter. The FTID controller outperforms traditional PID controllers in terms of transient and steady-state performance. The fault tolerance capabilities of the FTID controller will be tested on the CTTNL system in the future, using additive and multiplicative failures. Further research will include a comprehensive examination of robustness in the context of model parameter uncertainties.

Tomato Plant Leaf Disease Segmentation and Multiclass Disease Detection Using Hybrid Optimization Enabled Deep Learning.

Production of crops is increasing day by day in agriculture sectors. The insecurity of food is a main reason of plant disease and is a main global issue that humans face these days. With the design of contemporary environmental agriculture, more focus is devised for yielding the crop and elevating its quality. The occurrence of crops has elevated in years and the kind of disease has become more and more complex. The disease in plants and the pernicious insects are the major risks in agriculture field. Thus, earlier discovery and treatment of this disease are imperative. The major design of Deep Learning (DL) model helped in detecting the plant disease and grants a dynamic tool with accurate results. This paper presents DL-assisted technique for detecting and classifying the tomato disease and used deep batch-normalized eLu Alex Net (DbneAlexnet) for classifying the tomato plant leaves. Initially, tomato plant leaf images are taken as an input from specific dataset represented and it is subjected to preprocessing phase to eliminate unwanted distortions using anisotropic filtering. Then, the segmentation is carried out using U-net, which is trained by Gradient-Golden search optimization (Gradient-GSO) Algorithm and it is incorporation of both Golden search optimization (GSO) and Gradient concept. Thereafter the segmented image is given to image augmentation process, where position augmentation and color augmentation are considered. Finally, the multiclass plant leaf disease is classified using DbneAlexnet and is trained using proposed Gradient Jaya- Golden search optimization (GJ-GSO). Here, the GJ-GSO is devised with the integration of Gradient concept, Jaya algorithm, and GSO algorithm. The proposed GJ-GSO-based DbneAlexnet outperformed highest accuracy of 92.4%, True positive rate (TPR) of 91.9%, True negative rate (TNR) of 92.2% and smallest False Positive Rate (FPR) of 0.078. Hence, the technique with unified segmentation and classification is effectual for identifying the plant disease and the empirical research verifies the benefits of the developed model.

Experimental analysis and optimization of abrasive waterjet deep hole drilling process parameters for SS AISI 316L

Recent breakthroughs in component downsizing and miniaturization emphasise the requirement for deep-hole drilling with an increased aspect ratio, especially in the automobile sector for fuel injectors and the medical sector for manufacturing bone screws or surgical equipment. In this research, deep hole drilling on stainless steel AISI 316L has been conducted using an Abrasive Waterjet Machine (AWJM), and the influence of drilling variables on the material removal rate, roundness deviation of drilled holes have been evaluated to assess the machining and hole characteristics. In addition, machining parameters' statistical relevance has been investigated using a multi-parametric analysis of variance. Quadratic mathematical models for material removal rate and roundness deviation have been established by correlating drilling parameters. The Grey Wolf Optimization (GWO) algorithm has been applied in this research to identify the optimal combination of deep hole drilling parameters for maximizing material removal rate and minimizing the roundness deviation. The results have been compared with Derivative-free optimization, Whale optimization, and Harmony search algorithms. The comparison revealed that the GWO algorithm performed better than other algorithms. In addition, a validation test has been carried out to confirm the accuracy of the results produced by the GWO. The images obtained from the scanning electron microscope showed that the surfaces of the deep holes are smooth. Additionally, the ploughing action was shown to be the principal mechanism responsible for removing the material.

Capacity Allocation Optimization of Wind-Solar-Hydrogen-Storage Coupled Power Generation System Based on a Double-layer Model

Grid-integrated wind-solar and hydrogen storage coupling power generation systems face problems such as high costs of investment, construction, operation, and maintenance. After the conformity of renewable energy, the system’s dependability decreases, and the comprehensive power fluctuation influences the power grid greatly. To solve the above problems, this paper proposes a two-tier model. With the system economy, reliability, and wind-solar comprehensive power fluctuation suppression as optimization objectives, the capacity distribution of the hydrogen storage devices can be optimized. Based on a practical microgrid example, the inner and outer layers adopt Sparrow Search Algorithm (SSA) and Multi-objective Optimization Particle Swarm Optimization Algorithm (MOPSO), respectively, to solve the established model. The optimization results are compared according to three scenarios. The results demonstrate that this model can enhance the operational reliability of the system while considering the overall economy of the system and effectively suppressing the fluctuation of solar and wind power.

Energy Management Study of Interconnected Microgrids Considering Pricing Strategy Under the Stochastic Impacts of Correlated Renewables

The exponential augmentation of electricity consumption and the restructuring of the conventional power industry resulted in the emergence of microgrids (MGs). The nondispatchability and random attributes of the integrated renewable energy resources (RERs) challenge MGs scheduling operation, for which the multi-MGs system's energy management (EM) study has been gaining paramount importance and studied in this article. This article contemplates both internal and external markets for MGs’ effective participation in energy trading, which involves the energy exchange among MGs and that with the utility grid (UG). The energy pricing considers two conflicting objectives: MGs’ goal to improve their economy by reducing their purchasing prices and reliance on UG; the distribution network operator's aim to maximize its profit from the deployed market. Also, this article implements hybrid scenario and copula-based Monte Carlo techniques to assess the intermittencies associated with load demands, plug-in hybrid vehicle charging demands, and correlated RERs generations. The EM framework is formulated as a max–min optimization problem solved by a metaheuristic fuzzified jellyfish search optimization algorithm. Several simulation outcomes under different charging scenarios are reported, suggesting a 1.5% and 3.2% reduction in the multi-MG system's operational cost compared with the particle swarm optimization algorithm considering the best charging strategy during the summer and winter seasons.

Simulated Annealing Search Technique

Simulated Annealing is a stochastic global search optimization algorithm. The algorithm is inspired by annealing in metallurgy where metal is heated to a high temperature quickly, then cooled slowly, which increases its strength and makes it easier to work with. algorithm comes from annealing in metallurgy, a technique involving heating and controlled cooling of a material to alter its physical properties. Keywords: optimization, bound-constrained, local minima, travelling salesman problem.

An efficient load balancing in cloud computing using hybrid Harris hawks optimization and cuckoo search algorithm

An efficient load balancing in cloud computing using hybrid Harris hawks optimization and cuckoo search algorithm

A Fractional Ebola Optimization Search Algorithm Approach for Enhanced Speaker Diarization

A Fractional Ebola Optimization Search Algorithm Approach for Enhanced Speaker Diarization

Load Shedding Technique for Power System Using Neural Network Improved by Cuckoo Search Algorithm

The present paper introduces a load shedding methodology that leverages an upgraded neural network that relies on the Cuckoo search (CS) optimization algorithm to compare the efficiency and applicability with other methods in terms of speed and feasibility. The proposed method will be tested on the IEEE-37 bus system. The results of the method are compared with other optimization methods. Thereby, this method gives good results and feasibility in application. The criteria of voltage are considered, specifically the sensitivity index dV/dQ is proposed to find weak buses in the system that need to be relieved of the active power burden. Then, the shedding priority bus ranking is created to ensure the most favorable load shedding plan for the system to maintain voltage stability. Besides, the frequency parameter is also considered to calculate the optimal amount of shed load. The model system was tested by using POWERWORLD software. After comparing the results with other methods outlined in the paper, it has been determined that the proposed approach is highly effective for optimizing grid shedding in the system.

Interoperable IoMT Approach for Remote Diagnosis with Privacy-Preservation Perspective in Edge Systems.

The emergence of the Internet of Medical Things (IoMT) has brought together developers from the Industrial Internet of Things (IIoT) and healthcare providers to enable remote patient diagnosis and treatment using mobile-device-collected data. However, the utilization of traditional AI systems raises concerns about patient privacy. To address this issue, we present a privacy-enhanced approach for illness diagnosis within the IoMT framework. Our proposed interoperable IoMT implementation focuses on optimizing IoT network performance, including throughput, energy consumption, latency, packet delivery ratio, and network longevity. We achieve these improvements using techniques such as device authentication, energy-efficient clustering, environmental monitoring using Circular-based Hidden Markov Model (C-HMM), data verification using Awad's Entropy-based Ten-Fold Cross Entropy Verification (TCEV), and data confidentiality using Twine-LiteNet-based encryption. We employ the Search and Rescue Optimization algorithm (SRO) for optimal route selection, and the encrypted data are securely stored in a cloud server. With extensive network simulations using ns-3, our approach demonstrates substantial enhancements in the specified performance metrics compared with previous works. Specifically, we observe a 20% increase in throughput, a 15% reduction in packet drop rate (PDR), a 35% improvement in network lifetime, and a 10% decrease in energy consumption and delay. These findings underscore the efficacy of our approach in enhancing IoT network interoperability and protection, fostering improved patient care and diagnostic capabilities.

Optimal allocation of photovoltaic generations in buildings‐to‐distribution‐network integration system using improved backtracking search optimization algorithm

AbstractA novel optimal allocation framework for photovoltaic generations in an integration system of buildings‐to‐distribution‐network using improved backtracking search optimization algorithm is proposed here. In the proposed framework, photovoltaic generations are optimally allocated to optimize the overall performance of a buildings‐to‐distribution‐network regarding the efficient active power usage of photovoltaic generations, the energy savings, and voltage profile improvement of distribution network. The effects of building active demand response on the photovoltaic generations' optimal allocation are considered in the proposed framework. An improved backtracking search algorithm using two new operators is developed to optimize the active power reduction factors and locations of photovoltaic generations. The test results of IEEE 33‐bus and 69‐bus systems demonstrate that the developed framework can take full advantage of photovoltaic generation power and the active demand response of buildings to coordinate the efficient active power usage of photovoltaic generations, the voltage profile improvement and energy savings of a buildings‐to‐distribution‐network. In addition, the improved backtracking search optimization algorithm converges faster than genetic algorithm, classical backtracking search optimization algorithm, and bird swarm algorithm.

Modeling and structural optimization design of switched reluctance motor based on fusing attention mechanism and CNN-BiLSTM

Six-degrees of freedom (6-DOF) parallel mechanisms driven by switched reluctance motors (SRMs) can realize flexible control with high precision. Efficiency is an important indicator to measure the speed control system of SRMs. There are many characteristic factors affecting efficiency and strong nonlinear relationships between different characteristic parameters, which makes it difficult for analytical models and traditional neural network models to express their spatial correlation. For this reason, a convolutional neural network (CNN)-bidirectional long short-term memory network (BiLSTM) efficiency regression prediction model (CNN-BiLSTM-SENet) that integrates the attention mechanism (SENet) is proposed. Firstly, customize a formula for sensitivity analysis to screen characteristic parameters of efficiency. Secondly, build the CNN-BiLSTM-SENet model and use sparrow search algorithm (SSA) for hyperparameter optimization, input data to CNN to extract high-dimensional feature vectors that reflect complex changing relationships between features and efficiency while establishing feature channels. Embed the SENet to adaptively perceive and assign different weights to the feature channels, enhancing the influence of key features. Input the feature vectors outputted by the front-end network to BiLSTM to bidirectionally learn coupling relationships between sequences and complete regression prediction. Finally, propose improved northern goshawk optimization (MNGO) to solve the regression model to obtain the maximum efficiency and corresponding characteristic parameters. The results proved that the SSA-optimized CNN-BiLSTM-SENet model has higher fitting exactness and better prediction effect for efficiency regression prediction, and the MNGO also has stronger search ability and faster convergence.

Hybrid feature integration model and adaptive transformer approach for emotion recognition with EEG signals

Nowadays, electroencephalogram (EEG) signals are the major part of recognizing emotions as the signals contain more brain-related information. Several traditional recognition methodologies have been developed, but still, exist with certain challenges that degrade and mislead the recognition process. Some models are facing issues based on insignificant features, poor generalization capability, and computational burden. To alleviate such issues, an effective emotion recognition model is proposed using a heuristic-aided transformer-based approach. Initially, the input EEG signals are gathered and decomposed via the 5-level Discrete Wavelet Transform (DWT). Then, the decomposed signals are fed to the feature extraction process, where the feature extraction is carried out by using Logistic Regression (LR) approach. From the decomposed signals, the Logistic Regression (LR) is used for getting the noteworthy features and then, the same decomposed signals are fed to the optimal weighted feature selection, which is carried out using Adaptive Bald Eagle Search Optimization (ABESO) algorithm. Subsequently, the hybrid weighted feature selection approach is introduced for getting the weighted features, where the weight is optimized with the adoption of the ABESO algorithm. At last, the selected hybrid weighted features are sent to the recognition phase, where the Optimized Block Recurrent Transformer (OBRT), where performance gets enhanced by tuning the parameter with the developed ABESO algorithm. The accuracy and precision rate of the designed approach are attained as 96% and 94%. The performance of the model is assessed and the findings have shown that better performance that makes the system more feasible to recognize human emotions.

Performance analysis of buck converter with fractional PID controller using hybrid technique

In this paper, a hybrid technique is proposed for analyzing the performance of buck converters with fractional-order proportional integral derivative (FOPID) controllers. The hybrid approach is a combination of Capuchin Search Algorithm (CapSA) and Golden Jackal Optimization (GJO). The update behavior of Golden Jackal Optimization2 is enhanced with Capuchin Search Algorithm (CapSA) therefore it is called the improved GJO (IGJO) technique. The power converters are hard to manage based on their nonlinear nature and therefore the search for smart and effectual controllers is ongoing and continual. In current years, fractional order controllers have shown greater efficiency in power electronic systems. The IGJO technique is used to establish the optimum design of a fractional-order proportional integral derivative (PID) controller for the buck converter. The FOPID controller parameters are considered to diminish several performance metrics, with a particular focus on the Integral Squared Error (ISE). The proposed method is performed in the MATLAB, and its execution is analyzed by using the existing methods. From the simulation result, the proposed method reduces the error more effectively than existing methods.

Physics-inspired Metaheuristics for Construction Site Layout Planning Problem

In the construction industry, material handling plays an important role. Finding proper locations for construction facilities not only can affect the expenses, but also it can impact on the process of handling of construction materials. Therefore, in order to supply engineering demands and materials, the construction site layout planning problem (CSLP) within a short-distance transportation is considered as an NP-hard problem. Thus, the researchers are extensively using metaheuristics in order to solve the construction site layout planning problems. This study presents a comparative study of ten physics-inspired metaheuristics with regard to their efficacy in how they can address a real construction site layout problem. In this vein, two case studies are examined in terms of the site layout planning. Finally, the findings reveal that Gravitational Search Algorithm (GSA) and Thermal Exchange Optimization (TEO) have the ability to come up with better solutions, in comparison to other considered optimization algorithms.

A novel chaotic transient search optimization algorithm for global optimization, real-world engineering problems and feature selection.

Metaheuristic optimization algorithms manage the search process to explore search domains efficiently and are used efficiently in large-scale, complex problems. Transient Search Algorithm (TSO) is a recently proposed physics-based metaheuristic method inspired by the transient behavior of switched electrical circuits containing storage elements such as inductance and capacitance. TSO is still a new metaheuristic method; it tends to get stuck with local optimal solutions and offers solutions with low precision and a sluggish convergence rate. In order to improve the performance of metaheuristic methods, different approaches can be integrated and methods can be hybridized to achieve faster convergence with high accuracy by balancing the exploitation and exploration stages. Chaotic maps are effectively used to improve the performance of metaheuristic methods by escaping the local optimum and increasing the convergence rate. In this study, chaotic maps are included in the TSO search process to improve performance and accelerate global convergence. In order to prevent the slow convergence rate and the classical TSO algorithm from getting stuck in local solutions, 10 different chaotic maps that generate chaotic values instead of random values in TSO processes are proposed for the first time. Thus, ergodicity and non-repeatability are improved, and convergence speed and accuracy are increased. The performance of Chaotic Transient Search Algorithm (CTSO) in global optimization was investigated using the IEEE Congress on Evolutionary Computation (CEC)'17 benchmarking functions. Its performance in real-world engineering problems was investigated for speed reducer, tension compression spring, welded beam design, pressure vessel, and three-bar truss design problems. In addition, the performance of CTSO as a feature selection method was evaluated on 10 different University of California, Irvine (UCI) standard datasets. The results of the simulation showed that Gaussian and Sinusoidal maps in most of the comparison functions, Sinusoidal map in most of the real-world engineering problems, and finally the generally proposed CTSOs in feature selection outperform standard TSO and other competitive metaheuristic methods. Real application results demonstrate that the suggested approach is more effective than standard TSO.

VBQ-Net: A Novel Vectorization-Based Boost Quantized Network Model for Maximizing the Security Level of IoT System to Prevent Intrusions

Data sharing with additional devices across wireless networks is made simple and advantageous by the Internet of Things (IoT), an emerging technology. However, IoT systems are more susceptible to cyberattacks because of their continued growth and technological advances, which could lead to powerful assaults. An intrusion detection system is one of the key defense mechanisms for information and communications technology. The primary shortcomings that plague current IoT security frameworks are their inability to detect intrusions properly, their substantial latency, and their prolonged processing time and delay. Therefore, this work develops a clever and innovative security architecture called Vectorization-Based Boost Quantized Network (VBQ-Net) for protecting IoT networks. Here, a Vector Space Bag of Words (VSBW) methodology is used to reduce the dimensionality of features and identify a key characteristic from the featured data. In addition, a brand-new classification technique, called Boosted Variance Quantization Neural Networks (BVQNNs), is used to classify the different types of intrusions using a weighted feature matrix. A Multi-Hunting Reptile Search Optimization (MH-RSO) algorithm is employed during categorization to calculate the probability value for selecting the right choices while anticipating intrusions. In this study, the most well-known and current datasets, such as IoTID-20, IoT-23, and CIDDS-001, are used to validate and evaluate the effectiveness of the proposed methodology. By evaluating the proposed approach on standard IoT datasets, the study seeks to address the limitations of current IoT security frameworks and provide a more effective defense mechanism against cyberattacks on IoT systems.

Rapid Detection of Iron Ore and Mining Areas Based on MSSA-BNVTELM, Visible—Infrared Spectroscopy, and Remote Sensing

The accuracy and rapidity of total iron content (TFE) analysis can accelerate iron ore production. Although the conventional TFE detection methods are accurate, its detection speed presents difficulties in meeting production requirements. Therefore, this paper proposes a method of TFE detection based on reflectance spectroscopy (wavelength range: 340–2500 nm) and remote sensing. Firstly, spectral experiments were conducted on iron ore using the HR SVC-1024 spectrometer to obtain spectral data for each sample. Then, the spectra were smoothed and dimensionally reduced by using wavelet transform and principal component analysis. To improve the detection accuracy of TFE, a two hidden layer extreme learning machine with variable neuron nodes based on an improved sparrow search algorithm and batch normalization optimization (MSSA-BNVTELM) is proposed. According to the experimental results, MSSA-BNVTELM exhibited superior detection accuracy in comparison to other algorithms. In addition, this research established a remote sensing detection model using Sentinel-2 data and MSSA-BNVTEM to detect the distribution of TFE in the mining area. The distribution of TFE in the mine area was plotted based on the detection results. The results show that the remote sensing of the mine area can be useful for detection of the TFE distribution, providing assistance for the mining plan.

Modeling and optimization of a hybrid solar-battery-diesel power system for remote consumers.

Hybrid power systems can be affected by various uncertain parameters such as technical, economic, and environmental factors. These parameters may have both positive and negative impacts on the overall performance of the system. Therefore, in this study, an effective optimization method for modeling and optimization of a hybrid solar-battery-diesel power structure for remote consumers is proposed. The purpose of this method is to find the optimal configuration of the hybrid structure from the technical-economical-environmental point of view to cover the load demand requirements of remote consumers. To verify the performance of the hybrid microgrid, the results of the hybrid system based on the hourly meteorological data and load profile are compared with the results of the conventional diesel generator (DG). The optimization problem is solved using a harmony search optimization algorithm. The results show that the solar/battery/DG system is more suitable than the DG-only system in different uncertainty indexes based on economic and environmental aspects. Also, by the increase of the uncertainty index from 0.5 to 10%, the optimal total cost of the system decreases by 36.5%, while at the same time the unavailability value increases from 0.2259% to 9.9568%. Additionally, when the interest rate value increases from 1 to 7%, the total cost value increases around 17. 4%.