Differential Search Algorithm Research Articles

A novel enhanced performance-based differential search algorithm for the optimization of multiple renewable energy sources-based hybrid power system

A novel enhanced performance-based differential search algorithm for the optimization of multiple renewable energy sources-based hybrid power system

Online allocation of teaching resources for ideological and political courses in colleges and universities based on differential search algorithm

Online allocation of teaching resources for ideological and political courses in colleges and universities based on differential search algorithm

Minimized Group Delay FIR Low Pass Filter Design Using Modified Differential Search Algorithm

Designing a finite impulse response (FIR) filter with minimal group delay has proven to be a difficult task. Many research studies have focused on reducing pass band and stop band ripples in FIR filter design, often overlooking the optimization of group delay. While some works have considered group delay reduction, their approaches were not optimal. Consequently, the achievement of an optimal design for a filter with a low group delay value still remains a challenge. In this work, a modified differential search optimization algorithm has been used for the purpose of designing a minimal group delay FIR filter. The results obtained have been compared with the classical techniques and they turned out to be promising.

Energy-Efficient Mobile Data Ferry Pathway Construction Protocol Based on Transmission Coverage and Hybrid Differential Search Algorithm in Wireless Sensor Networks

Energy-Efficient Mobile Data Ferry Pathway Construction Protocol Based on Transmission Coverage and Hybrid Differential Search Algorithm in Wireless Sensor Networks

Optimal Intelligent Vehicle Control System for Emergency Vehicle Using Visible Light Communication VANET

Abstract: Traffic congestion is one of the significant problems in every metropolitan city. Traffic congestion occurs, when large numbers of vehicles are all together and are not able to move or move slowly, it is also known as a traffic jam. The main aim of the proposed OIVC-VLC VANET system is to improve the data transmission rate to control traffic in high density loads for emergency vehicles. Traffic congestion leads to wasting of time, road accidents, delays of trips, and reduces regional economic health and fuel consumption. Moreover, the most critical concern of traffic congestion is a delay of emergency vehicles like ambulance and police vehicles, and firefighting trucks leading to an increase the human death and loss their essential things. In addition, this paper focuses to reduce the traffic congestion and traveling time of emergency vehicles. To overcome traffic congestion issues, an Optimal Intelligent Vehicle Control System for the emergency vehicle by intelligent traffic clearance (OIVC-VLC-VANET) is proposed. Firstly an improved whale optimization (IWO) algorithm is submitted for grouping the vehicle nodes based on their behaviors. Secondly, the differential search algorithm is used to select the next forwarding node using multiple constraints received from vehicles. Thirdly, the dragonfly algorithm is submitted for avoiding extra time due to the control packets exchange process. The results show that the proposed OIVC-VLC-VANET system can perform very efficiently in terms of quality constraints than existing systems.

Estimating model parameters from SP anomaly of sheet-shaped sources using differential search and particle swarm optimization algorithms

Abstract In this study, the efficiency of estimating the model parameters of sheet-shaped single and multiple sources of the self-potential (SP) anomaly using the differential search algorithm (DSA) is investigated. First, noise-free and noisy synthetic anomalies are calculated for a single sheet-shaped source, and its model parameters estimated by DSA. The DSA inversion is also done for a model consisting of three inclined sheets. To test the effectiveness of the method, the same processes are repeated with a more conventional algorithm, particle swarm optimization (PSO), and the solutions of both methods are compared. The results of synthetic anomaly analyses show that DSA can predict the parameters as accurately as PSO. Then, both algorithms are also applied to two field SP anomalies (Surda and Beldih) that have been evaluated by different algorithms in the literature. The source of the Surda anomaly is modelled as one sheet, whereas the source model of the Beldih anomaly is assumed to consist of three sheets. The five model parameters for each model are estimated using both algorithms and it is determined that they are in good agreement with the findings of the previous studies. The contribution of the regional background anomaly to the synthetic and field anomalies are also included and regional coefficients are estimated. Finally, we conclude that DSA can solve the source parameters without the need for the initial values required in conventional iterative inversion methods and is an efficient and promising algorithm for determining the parameters of SP sources.

Maiden application of colliding bodies optimizer for load frequency control of two‐area nonreheated thermal and hydrothermal power systems

AbstractThis article presents a novel load frequency control (LFC) approach using colliding bodies optimizer (CBO) for frequency stabilization of interlinked multiarea electric power systems. The optimal parameters of the suggested CBO‐based proportional–integral–derivative‐filter controller ascertain an effective LFC solution. First, a well‐known and widely used linearized two‐area nonreheated thermal power system is examined to demonstrate the efficacy of the proposed approach. The effectiveness of the proposed method is analyzed by comparing the outcomes of several recently presented LFC schemes. The performance analysis shows a settling time improvement of 3.10%, 14.29%, and 18.66% in the case of area‐1 and area‐2 frequencies and tie‐line power deviations compared with an imperialist competitive algorithm (ICA)‐based controller. The robustness of the proposed scheme is also evaluated in the presence of various operating scenarios. Additionally, the work is extended to a two‐area nonreheated hydrothermal power system. The proposed method shows an improvement of over 60% in the performance index compared with several existing techniques‐based controllers such as optics‐inspired optimization, gray‐wolf optimization, quasi‐oppositional differential search algorithm, bacterial foraging optimization algorithm, and ICA.

Online allocation of teaching resources for ideological and political courses in colleges and universities based on differential search algorithm

Online allocation of teaching resources for ideological and political courses in colleges and universities based on differential search algorithm

Design of Cross‐Coupled Nonlinear PID Controller Using Single‐Objective Evolutionary Algorithms

The effectiveness of evolutionary algorithms (EAs) such as differential search algorithm (DSA), Real‐Coded genetic algorithm with simulated binary crossover (RGA‐SBX), particle swarm optimization (PSO), and chaotic gravitational search algorithm (CGSA) on the optimal design of cross‐coupled nonlinear PID controllers is compared in this paper. A cross‐coupled multivariable PID controller structure for the binary distillation column was developed with two inputs and two outputs. EA simulations are run to lower IAE using two stopping criteria, namely, maximum number of functional evaluations (Fevalmax) and Fevalmax plus PID parameter and IAE tolerance. Over 20 separate trials were used to compare the performances of several EAs using statistical measures such as best, mean, standard deviation of outcomes, and average calculation time. This article presents the design of a cross‐coupled nonlinear PID controller using single‐objective evolutionary algorithms. Using evolutionary algorithms (EAs) with a multicrossover strategy, the results achieved by various EAs are compared to previously reported results. The results of a multivariable cross‐coupled system clearly show that a single‐objective nonlinear PID controller performs better. Simulations further show that all four techniques evaluated are suitable for PID controller tweaking off‐line. However, only the single‐objective evolutionary algorithms are acceptable for online PID tuning due to their higher consistency and shorter computation time.

Real-Time State of Charge Estimation of Lithium-Ion Batteries Using Optimized Random Forest Regression Algorithm

This paper presents an improved machine learning approach for the accurate and robust state of charge (SOC) in electric vehicle (EV) batteries using differential search optimized random forest regression (RFR) algorithm. The precise SOC estimation confirms the safety and reliability of EV. Nevertheless, SOC is influenced by numerous factors which cannot be measured directly. RFR is suitable for real-time SOC estimation due to its robustness to noise, overfitting issues and capacity to work with huge datasets. However, proper selection of RFR architecture and hyper-parameters combination remains a key issue to be explored. Hence, a differential search algorithm (DSA) is employed to search for the optimal values of trees and leaves in the RFR algorithm. DSA optimized RFR eliminates the utilization of the filter in data pre-processing steps and does not require a detailed understanding and knowledge about battery chemistry, rather only needs sensors to monitor battery voltage and current. The developed approach is validated at room temperature using two types of lithium-ion batteries under a pulse discharge test. In addition, the proposed model is verified under varying temperature settings under EV drive cycles. The experimental results demonstrate that the DSA optimized RFR algorithm achieves RMSE of 0.382% in the HPPC test using LiNMC battery. Besides, the proposed method obtains satisfactory outcomes in EV drive cycles, estimating MAE of 0.193% and 0.346% in DST and FUDS cycles, respectively, at 25°C.

Interpretation of volcanic magnetic anomalies using differential search algorithm: case study from the Kula volcanic park, western Türkiye

Interpretation of volcanic magnetic anomalies using differential search algorithm: case study from the Kula volcanic park, western Türkiye

Open pollution routing problem of logistics distribution in medical union based on differential search algorithm

Medical care is a guarantee of people's daily life. Improving healthcare contributes to people's well-being. However, healthcare resources are characterized by uneven distribution. Financially well-off areas will have higher quality health care resources. Most of the medical resources are concentrated in public general hospitals, however, primary care institutions can hardly meet the growing needs of people. To solve this problem, Medical Union achieves efficient deployment of resources by integrating various medical institutions in the same area. In the process of logistics integration of the medical union, the scale of logistics distribution expands accordingly. Transportation vehicles have high operating costs and produce greenhouse gases in the process of distribution. The optimization of the driving path of logistics distribution vehicles can reduce the operating cost, fuel consumption and carbon emission. To solve this kind of decentralized and complex vehicle routing problem, this paper proposes a pollution routing problem model considering electrical vehicle usage, customer's soft time window expectation, open path and carbon cost. A modified Differential Search Algorithm with the comprehensive learning strategy and dynamic Cauchy variation strategy is advanced to solve the problem. Results show that the improved algorithm has good solving performance, and verifies the rationality of the proposed model, which will help to reduce carbon emissions and save the logistics and operating costs of medical devices.

Sophisticated methods for noise filtering, subgroup discovery, and classification in big data analysis

Big Data is a popular research area where a vast amount of data is created, replicated, and consumed by society. The quality of the data used directly influences big data knowledge discovery. The existence of noise is the most prevalent problem influencing data quality. The following techniques were developed to reduce noise in data with a distributed setting: Homogenous Ensemble for Big Data (HME-BD) and Heterogeneous Ensemble for Big Data (HTE-BD). In this article, the performance of HTE-BD is improved further by developing Enhanced HTE-BD (EHTE-BD), which combines Logistic Regression based Support Vector Machine (LR-SVM) in conjunction with RF, LR, and KNN to reduce noisy data. Furthermore, the Multi-Objective Evolutionary Fuzzy Method for Subgroup Discovery throughout Big Data (MEFASD-BD) was used to resolve the multi-objective optimization challenge, and the Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) was utilized to handle the rising dimensionality issue through subgroup discovery. To address the NSGA-II’s slow convergence rate, an Improved Multi-Objective Meta-Heuristic Fuzzy approach for discovering subgroups in big data is described, that contains a meta-heuristic method for subgroup discovery known as the Multi-Objective Differential Search Algorithm (MODSA). It selects the most relevant subgroups from vast amounts of data, reducing the data’s dimensionality. The Fuzzy Deep Neural Network (FDNN) classifier assesses the main subgroups. By removing noisy data and selecting the most relevant subgroups, the performance of FDNN in classifying vast amounts of data is improved.

Optimal Choice and Allocation of Shunt FACTS Devices Using Differential Search Algorithm

In this paper, new and efficient heuristic algorithm, Differential Search Algorithm (DSA), is used for optimal choice and allocation of shunt Flexible AC transmission systems (FACTS) devices, Static Var Compensator (SVC) and Static Synchronous Compensator (STATCOM). In the proposed optimal location method, SVC and STATCOM devices are located randomly and dynamically with objective of minimizing the real power loss, improving the voltage profile and enhancing the voltage stability. The best solution sets for objectives aforementioned are achieved by minimizing each objective functions by the proposed approach. In order to evaluate the proposed methodology, it has been tested on IEEE 30 bus system and compared to another widely used optimization algorithm, Artificial Bee Colony (ABC) algorithm. The validity of the proposed optimization algorithm is proven by the results and comparative analysis with other method.

OQR-SC: An Optimal QoS Aware Routing Technique for Smart Cities Using IoT Enabled Wireless Sensor Networks

Internet of things (IoT) makes a machines optimization in everyday which processing the data by very intelligently and make communication more effectively and efficiently. In smart city environments, the data collection and communication play an important role in defining quality. Since the research period, it has been recommending a new data acquisition and data communication software framework for IoT smart applications. For further improvements, we recommend an optimal QoS aware routing technique for smart cities using IoT enabled wireless sensor networks (OQR-SC). In data gathering phase, the proposed system introduce chaotic bird swarm optimization (CBSO) algorithm for IoT sensor cluster formation; the improved differential search (IDS) algorithm used to estimate the faith degree of each sensor node, the highest trust node act as cluster head (CH). In data transferring phase, first illustrates lightweight signcryption technique for data encryption between two IoT sensors. Then, we use optimal decision making (ODM) algorithm to compute the optimal path between source–destination in IoT platform. Finally, the proposed OQR-SC technique is implemented using network simulation (NS2) tool and analyzes the performance of proposed technique with existing state-of-art techniques. The result summarizes that average energy consumption of proposed OQR-SC technique is 12.39% lower than the existing techniques; the average network lifetime of proposed OQR-SC technique is 15.96% higher than the existing techniques; the average delay of proposed OQR-SC technique is 21.08% lower than the existing techniques; and the average throughput of proposed OQR-SC technique is 17.89% higher than the existing techniques.

A New and Simple Mathematical Technique to Study the Steady-state Performance of Isolated Asynchronous Generator

Isolated Asynchronous Generator (IAG) is nowadays widely used for renew-able power generation from the sources like wind and small hydro. Tradi-tionally, the steady-state analysis of IAG is carried out by solving a complexhigher order non-linear polynomial equation obtained in a complicated wayfrom the steady-state circuit diagram. In this paper, a simple new mathe-matical procedure has been introduced to obtain two non-linear polynomialequations in much more simplified form which can easily be solved forthe unknown variables i.e., per unit generated frequency (a) and magnetiz-ing reactance (Xm). Between these two equations, one equation comprisesonly one unknown variable‘a’ and hence, easy to solve. Differential SearchAlgorithm (DSA) has been efficiently implemented for solving these non-linear equations. The computational efficacy of DSA has been compared withthat of Newton-Raphson (N-R) method, Linear Search Algorithm (LSA),Binary Search Algorithm (BSA) and Particle Swarm Optimization (PSO) technique. The performance of the IAG has been studied under differentoperating conditions such as variation of speed, capacitance and load. Allthe simulated results have been experimentally verified using a three-phase,415 volts, 2.2 kW, star-connected induction generator and a close agreementhas been found.

Multicamera Calibration Optimization Method Based on Improved Seagull Algorithm

There are some problems in the process of camera calibration, such as insufficient accuracy and poor accuracy. Based on the seagull algorithm, the adaptive differential evolution algorithm is combined with the seagull algorithm to optimize the multicamera calibration. The seagull algorithm can achieve good results on multiparameter problems and effectively avoid falling into local optima. In this paper, the adaptive differential search algorithm is adopted to improve the local search ability and optimize the local search and global search ability. According to Zhang Zhengyou's method, the calibrated parameter is obtained, in which the parameter is used as the initial value. Then, taking the minimum mean error as the criterion, the improved seagull algorithm (SOA-SaDE) is used to establish the objective function, and the internal parameters and distortion coefficient of the camera are further solved. Verification experiments showed that the fusion algorithm has less reprojection error and higher calibration accuracy gull algorithm.

Automated test design using swarm and evolutionary intelligence algorithms

AbstractThe world's increasing dependence on computer‐assisted education systems has raised significant challenges about student assessment methods, such as automated test design. The exam questions should test the students' potential from various aspects, such as their intellectual and cognitive levels, which can be defined as attributes of the questions to assess student knowledge. Test design is challenging when various question attributes, such as category, learning outcomes, difficulty, and so forth, are considered with the exam constraints, such as exam difficulty and duration. In this paper, four contributions are provided to overcome test design challenges for the student assessment. First, a tool is developed to generate a synthetic question pool. Second, an objective function is designed based on the considered attributes. Third, the popular swarm and evolutionary optimization methods, namely particle swarm optimization, genetic algorithm, artificial bee colony, differential search algorithm are comparatively studied with novel methodologies applied to them. Finally, as the state of the art methods, artificial bee colony, and differential search algorithm are further modified to improve the solution of the test design. To perform the proposed algorithms, a dataset of 1000 questions is built with the proposed question attributes of the test design. Algorithms are evaluated in terms of their successes in both minimizing the objective function and running time. Additionally, Friedman's test and Wilcoxon rank‐sum statistical tests are applied to statistically compare the algorithms' performances. The results show that the improved artificial bee colony and the improved differential search provide better results than others in terms of optimization error and running time.

Region based multi-spectral fusion method for remote sensing images using differential search algorithm and IHS transform

Remote sensing applications such as clustering, classification, feature extraction, measurement and change detection need fused high-resolution images that contain complementary information coming from multi-spectral bands. To transfer as much information as possible from source images to the fused image, image fusion can be considered as an optimization problem. In this paper, a new, two-stage, multi-spectral, region based, optimal fusion method is proposed. Panchromatic and infrared bands of Landsat 8 satellite images are fused with K-medoids segmentation and Differential Search Algorithm (DSA). The fused image is combined with the red, green, and blue (RGB) bands of the same area with intensity-hue-saturation (IHS) transform. Experiments carried out on test images indicate that developed method performed better than most of the state-of-the-art fusion techniques in terms of both visual and numerical evaluations.

Accurate parameters extraction of PEMFC model based on metaheuristics algorithms

The electro-chemical proton exchange membrane fuel cell (PEMFC) is an inventing electrical generator from chemical reaction process as a green energy source. An accurate PEMFC model with its precise parameters should be used to carefully fitting of polarization curve to best study and design of its characteristics and performance. This paper introduces an accurate PEMFC model based on recent metaheuristics algorithms to evaluate precisely the unknown parameters of PEMFC. Algorithms of; Whale Optimization Algorithm (WOA) , Weighted Differential Evolution Algorithm (WDE) , Differential evolution algorithm with strategy adaptation (SADE) , Moth- Flame Optimization Algorithm (MFO) , adaptive differential evolution with optional external archive (JADE), Improved mine blast algorithm (IMBA), Gray Wolf Optimizer (GWO), Dragonfly algorithm (DA), Differential EVOLUTION ALGORITHM (DE) , Cumulative Population Distribution Information in Differential Evolution (CPIJDE) , Differential evolution based on covariance matrix learning (COBIDE) , Covariance Matrix Adaptation Evolution Strategy (CMA-ES) , Bernstain-search differential evolution algorithm (BSD), Backtracking Search Optimization Algorithm (BSA) , Bezier Search Differential Evolution Algorithm (BESD ) , DIFFERENTIAL SEARCH ALGORITHM (DSA) and Bijective DSA (B-DSA) , Biogeography-based optimization (BBO); have been applied to estimate model of PEMFC. The verification of the suggested optimizing algorithms is applied on three practical PEMFC stacks of BCS 500-W PEM, 500 W SR-12PEM and 250 W stacks, for different operating conditions. The accuracies of the PEMFC extracted parameters are measured in sum of square errors (SSE) between the results obtained by the optimizing parameters and the test results of the fuel cell stacks in the objective function. Also, the applied methods have been validated as compared results with different research works that were listed in literatures. Moreover, the polarization curves of the applied methods are clear and coinciding with manufacturing polarization curves for all the case study results. So, the suggested PEMFC optimizing model has superiority on the comparative models with respect to the system accuracy and convergence process.