Differential Evaluation Research Articles

A change detection algorithm for the SAR images based on DWT and DE optimization

ABSTRACT In this paper, a novel change detection algorithm is proposed for the remote sensing Synthetic Aperture Radar (SAR) images. Change detection in SAR images is one of the critical tasks in the field of remote sensing as the images contain significant speckle noise and illumination variations. In order to address these issues, a novel change detection algorithm is proposed using Discrete Wavelet Transform (DTW) and Differential Evaluation (DE). At first, the DWT is utilized to remove the speckle noise from the images. Then, a DE optimization-based Support Vector Machine (SVM) algorithm is proposed to detect the changes in SAR images. The proposed method can give comparatively better correct classification values, and lesser false alarm as well as total error values than the recently developed change detection methods.

Enhancing sustainable urban economies: A statistical investigation of transitioning from fossil fuel-dependent industries to green innovation hubs

As a result of the evolution of the smart grid, the new energy market has been restructured in urban systems, whereby domestic users play a more active role in the demand response (DR) programs with a view to balancing production and demand. DR incentive signals, though, are difficult to respond to due to limited understanding by users. As a result, an Energy Management Controller (EMC) has been expanded for responding to DR signals and solving energy management problems. The EMC employs a whale optimization algorithm (WOA) for automatically responding to the DR signal and solving energy management issues by planning 3 kinds of domestic load: non-interruptible, interruptible, and hybrid. With the WOA, the EMC generates the best home load plan for reducing power costs, carbon emissions, PARs, and customer dissatisfaction. Simulations were accomplished in order to evaluate the offered WOA against current algorithms such as particle swarm optimization (PSO), differential evaluation (DE), and teaching learn optimization algorithm (TLBO). The outcomes indicated that the suggested WOA reduces electricity costs, carbon emissions, PARs, and consumer dissatisfaction better than other algorithms.

DESAIN TUNING PID MENGGUNAKAN DIFFERENTIAL EVOLUTION (DE) UNTUK SISTEM PENGATURAN SUHU PADA INKUBATOR

The temperature in the room is very dependent on the temperature outside the room. If the temperature outside is unstable, the impact will be reflected on the temperature instability in the room. This creates challenges in controlling room temperature that require a level of consistency, such as in a baby incubator machine, oven, or egg hatcher. Thus, a temperature regulation strategy is needed to overcome disturbances arising from external temperature fluctuations. This research proposes a solution through the use of innovative control methods, namely by making conventional PID control and PID Disturbance Observer. The value of the PID constant will use the Differential Evaluation (DE) method. Both will be tested on an incubator system through simulations at MATLAB2014a with performance index criteria in the form of ITAE. Interference will be introduced into the system by magnifying the temperature difference between inside and outside the incubator so that heat transfer occurs which will affect the temperature inside the incubator. The results showed that the ITAE value of the Distubance Observer PID control was 0.035, while the ITAE of conventional PID control was 0.242. This shows that the PID Disturbance Observer control is an effective control in controlling the temperature inside the incubator and maintaining that temperature from outside interference.

Mitigation of Grid Current Harmonics by ABC- ANN based Shunt Active Power Filter

Harmonics are being introduced into power system networks as a result of the increasing use of nonlinear devices. These harmonics cause distortion of current and voltage signals, which in turn causes damage to power distribution systems. As a result, the suppression of harmonics is of extreme significance in power systems. This paper proposes Shunt Active Power Filters (SAPF) based on neural network algorithms like Artificial Neural Network (ANN) as a feasible approach to mitigating harmonic distortion and raising power quality in electrical distribution systems. This research shows that using shunt active power filters (SAPF), which use the Artificial Bee Colony Optimized Artificial Neural Network Controller (ABC-ANN), is an efficient method for enhancing power quality and minimizing harmonic distortion in distribution systems. The ABC-ANN algorithms have been produced for SAPF with the goal of improving system performance by reducing Grid current Harmonics .In the first stage of this work, PSO Technique is used to tune a standard PI controller to its optimum gain values (Ki ,Kp). Then, these target and input data of PSO tuned PI controller will be supplying inputs to the ANN controller. To find the optimal weight and bias values, this ANN controller has been tuned with the help of the ABC algorithm. Using MATLAB/SIMULINK software, we compare the performance of the proposed algorithm to that of other optimization algorithm like Differential Evaluation (DE) algorithm, as well as the PSO tuned PI controller and traditional PI controller. The findings from the simulation suggest that a SAPF utilizing an ABC trained ANN controller could improve THD in the supplying current while maintaining harmonics within IEEE-519 accepting levels.

A robust intelligence regression model for monitoring Parkinson’s disease based on speech signals

Parkinson’s disease (PD) is a degenerative neurological disease, and early diagnosis of PD is crucial. Monitoring PD progression from voice records is a promising technique, which is particularly suitable for IoT-based telemedicine in smart home. But, how to select the most relevant voice features fast and accurately for early diagnosis of PD is still an open question. In this study, a robust intelligent regression model is proposed to monitor PD patients from voice records. The proposed model consists of a binary version of an ant lion optimizer (BALO) for selection of voice features and an extreme learning machine (ELM) based on differential evaluation (DE) for continuous prediction of unified Parkinson scale (UPDRS). As compared with various machine learning (ML) prediction methods and meta-heuristic algorithms, the proposed BALO-DEELM method can select most predictive voice features and make prediction in a faster and more accurate manner, which is particularly suitable for IoT-based telemedicine. The BALO-DEELM model has the potential to be used as a crucial data analysis part in an IoT-based telemedicine system for PD monitoring.

Integration of Distributed Generation and Plug-in Electric Vehicles on Power Distribution System by Using Queuing Theory

Plug-in electric vehicles (PEVs) and distributed generation (DG) can positively and negatively impact the distribution system. Therefore, this paper presents the modeling and analysis of DG and PEVs’ penetration levels of the three-phase unbalanced radial distribution system. The study aims to optimize the distribution system’s DG sizing and PEV charging to minimize total power loss. The test system is the 4th circuit of the Nonsung service station along Thaharn Road, Muang District, Udon Thani, Thailand. According to objective function and constraints, such control variables as installation buses and rated outputs of DG and the penetration levels of PEVs were obtained to evaluate the total power loss. Significantly, the charging demand of PEVs is an uncertain load estimated by queuing theory integration with the minimization tool called the differential evaluation (DE) method. According to the result comparison of a four case simulation, the total power losses of queuing theory and DE application are minimum. Finally, total power losses conform to the regulation of the Provincial Electricity Authority (PEA), Thailand.

Optimal Environment Control Mechanism Based on OCF Connectivity for Efficient Energy Consumption in Greenhouse

Greenhouses are a productive system that allows us to respond to the growing global demand for fresh and healthy food throughout the year, but the greenhouse environment is not easily controlled because its climate parameters are interrelated. However, the numbers of the actuator are operated parallelly to maintain the greenhouse environment; as a result, the energy consumption of greenhouses is high. In this study, we presented the optimization module by considering the outdoor environment with the aim of minimum energy consumption. Metaheuristic-based differential evaluation (DE) is used to optimize the climate parameters by considering indoor and outdoor environmental constraints. Furthermore, the long short-term memory (LSTM)-based inference model is offloaded on the Internet of Things (IoT) device to predict the next environmental situation. The objective function selects the optimal parameters within user preferences with minimum energy consumption based on the inferred parameter value. The open-source software framework IoTivity, implementing open connectivity foundation (OCF) technical standards, is used for the real-time connection between IoT devices and the IoT platform. Greenhouse owners can set the preferences based on the requirements of plants in the greenhouse by using a smart and remotely accessible Android-based interface. A fuzzy logic-based control module operates on an IoT device that maps the optimized parameters with the actuator and operates accordingly. The proposed model is analyzed, and the performance is evaluated in terms of energy consumption for each climate parameter and actuator in the greenhouse. The results show that the proposed mechanism saves 36% of energy.

Performance Evaluation of an Improved ANFIS Approach Using Different Algorithms to Predict the Bonding Strength of Glulam Adhered by Modified Soy Protein–MUF Resin Adhesive

Despite studies on the potential replacement of synthetic resins by bio-based adhesives such as proteins in recent years, there is still no reliable method for estimating the strength of wood products made using the combined parameters in the literature. This limitation is due to the nonlinear relationship between strength and the combined components. In the present research, the application of artificial intelligence techniques was studied to predict the bonding strength of glulam adhered by protein containing different ratios of MUF (melamine–urea–formaldehyde) resin with different F-to-U/M molar ratios at different press temperatures. For this purpose, the ANFIS artificial intelligence model was used as basic mode or combined with ant colony optimization (ACOR), particle swarm optimization (PSO), differential evaluation (DE) and genetic algorithms (GA) to develop an optimal trained model to predict the bonding strength of glulam based on experimental results. Comparison of the obtained results with the experimental results showed the ability of the above methods to estimate the bonding strength of glulam in a reliable manner. Although the basic ANFIS alone and in combination with other algorithms was not able to achieve an ideal performance prediction to estimate bonding strength, the combination of GA and ANFIS offered an excellent ability compared to the combination of other algorithms combined with ANFIS. Hence, the developed ANFIS-GA model is introduced as the best prediction technique to solve bonding strength problems of laminated products. In addition, using the developed optimal model, a precise attempt was made to show the nature of the parameters used to produce glulam and determine the optimum limit.

Performance analysis of CMOS based analog circuit design with PVR variation

Process, supply voltage, and temperature (PVT) are three important factors which contribute to performance variation of the complementary metal–oxide–semiconductor (CMOS) based analog circuits. In this paper, CMOS based analog circuit design with the PVT variation effects are explored. The effects of the PVT variation on the performance of CMOS based analog circuits are introduced. The optimization of CMOS based analog circuits such as differential amplifier (DA) and two-stage operational amplifier (op amp) circuits with PVT variations with different algorithms such as cockoo search (CS), particle swam optimization (PSO), hybrid CSPSO, and differential evaluation (DE) algorithms is presented. Each algorithm is implemented using the C programming language, interfaced with Ngspice circuit simulator, and tested on the Intel®core™ i5, 2.40 GHz processor with 8 GB internal RAM using the Ubuntu operating system (OS). The result shows PVT variation affects the performance of CMOS circuit.

Optimal energy scheduling of a standalone rural microgrid for reliable power generation using renewable energy resources

ABSTRACT Rural villages can be electrified using renewable power sources for the area to grow effectively. Microgrids powered by renewable energy is more environmentally friendly and practical choices for electrifying rural areas. This study attempts to ascertain the technological and financial viability of an autonomous hybrid energy source for a remote Indian area in order to provide a reliable power supply. The region’s load requirements can be met with the help of a solar energy system, biogas, wind energy, diesel generators, and batteries. Differential evaluation (DE) is used to optimize the proposed microgrid for grid-independent operation. The system’s ultimate objective is to reduce energy costs and recommend workable component configurations. Particle swarm optimization (PSO) and the genetic algorithm (GA) are used to compare the DE findings. The finding suggested the optimal energy cost of 0.22 $/kWh, a net current cost of $5,18,656, and a capital cost of $8,50,774. DE’s total net present cost is 4.3% less than PSO and 5.2% less than GA. However, it has been determined that the recommended approach is more practical and affordable for electrifying rural areas.

A Modified LBP Operator-Based Optimized Fuzzy Art Map Medical Image Retrieval System for Disease Diagnosis and Prediction.

Medical records generated in hospitals are treasures for academic research and future references. Medical Image Retrieval (MIR) Systems contribute significantly to locating the relevant records required for a particular diagnosis, analysis, and treatment. An efficient classifier and effective indexing technique are required for the storage and retrieval of medical images. In this paper, a retrieval framework is formulated by adopting a modified Local Binary Pattern feature (AvN-LBP) for indexing and an optimized Fuzzy Art Map (FAM) for classifying and searching medical images. The proposed indexing method extracts LBP considering information from neighborhood pixels and is robust to background noise. The FAM network is optimized using the Differential Evaluation (DE) algorithm (DEFAMNet) with a modified mutation operation to minimize the size of the network without compromising the classification accuracy. The performance of the proposed DEFAMNet is compared with that of other classifiers and descriptors; the classification accuracy of the proposed AvN-LBP operator with DEFAMNet is higher. The experimental results on three benchmark medical image datasets provide evidence that the proposed framework classifies the medical images faster and more efficiently with lesser computational cost.

HIIDS: Hybrid intelligent intrusion detection system empowered with machine learning and metaheuristic algorithms for application in IoT based healthcare

This paper presents machine learning and metaheuristic algorithms based hybrid intelligent Intrusion Detection System (HIIDS) for Internet of Things based applications such as healthcare. In IoT based smart healthcare, biomedical sensors sense vital health parameters which are sent to the cloud server for storage and analysis. Health data saved as Electronic Health Record (EHR) is privacy and security sensitive. This work focuses on the detection of security attacks on cloud servers through anomaly based intrusion detection. Popular NSL-kDD dataset containing 41 features with 125,973 samples have been utilized for performance evaluation of proposed HIIDS.To reduce computation cost, metaheuristic algorithms such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Differential Evaluation (DE) are used for best feature selection and supervised learning algorithms such as Known Nearest Neighbor (kNN), Decision Tree (DT) are used for accurate classification of normal and attack class based on selected features. Also a hybrid approach has been presented for feature selection and classification. After dataset pre-processing using python, MATLAB 2019b is used to implement six variants of proposed hybrid algorithms combining GA, PSO, DE with kNN, DT. Performance evaluation has been done based on accuracy, execution time, memory usage and CPU utilization. GA-DT variant gives highest accuracy of 99.88%, 86.40%, 95.39%, 96.90%, 100% of accuracy for DoS, U2R,R2L, Probe and Normal class with the help of 8-10 features compared to other variants such as GA-kNN, PSO-kNN, PSO-DT, DE-kNN, DE-DT. Also outperforms similar state-of-the-art works in terms of classification accuracy, simulation results are given in support. Finally an IoT based healthcare architecture is designed using best performing hybrid GA-DT variant based HIIDS to detect and prevent malicious traffic.

Multi-level Kapur’s thresholding using whale optimization and social group optimization for brain MRI image segmentation

Image thresholding is the vital part for segmentation of a region in image. A fresh technique of multilevel thresholding based on the whale optimizer and social group optimizer are in fact recommended in this particular paper. New Optimization techniques inspired are actually affected as a result of the interpersonal as well as the backpacking behaviour of all of the whales. The process of multi-level thresholding problem is applied through the Kapur’s entropy and metaheuristic algorithms. The suggested strategy is tried on brain MRI test image set. The suggested methods are next in contrast with particle swarm optimization (PSO) and differential evaluation (DE) constructed multilevel thresholding techniques and other optimization techniques in the literature. The segmentation really calculated with evaluation measures like Sensitivity, Specificity, and Dice Score (DS).

Detecting Phishing Cyber Attack Based on Fuzzy Rules and Differential Evaluation

Cyber-criminal attacks witnessed various phishing attacks that blindly attract users to use their secret personal information. This research highlights this type of attack and proposes the development of a new model to detect phishing websites. Two aspects of improvement were followed. Firstly, a new feature is inferred from a combination of features with higher classification capability than the original features. Secondly, a novel method has been proposed by using differential evaluation (DE) to select the best rules among the fuzzy rule set; the DE is a viable way to optimize the fuzzy rule, improving the classification complexity. The proposed system was evaluated by applying two malicious and legitimate datasets collected from Phishtank and Alexa websites. Results show the combination feature can include a promising method for detecting phishing attacks according to the evaluation metrics in the precision of 0.973, recall of 0.933, F-measure of 0.947, and accuracy of 0.976.

A Cox Proportional-Hazards Model Based on an Improved Aquila Optimizer with Whale Optimization Algorithm Operators

Recently, a new optimizer, called the Aquila Optimizer (AO), was developed to solve different optimization problems. Although the AO has a significant performance in various problems, like other optimization algorithms, the AO suffers from certain limitations in its search mechanism, such as local optima stagnation and convergence speed. This is a general problem that faces almost all optimization problems, which can be solved by enhancing the search process of an optimizer using an assistant search tool, such as using hybridizing with another optimizer or applying other search techniques to boost the search capability of an optimizer. Following this concept to address this critical problem, in this paper, we present an alternative version of the AO to alleviate the shortcomings of the traditional one. The main idea of the improved AO (IAO) is to use the search strategy of the Whale Optimization Algorithm (WOA) to boost the search process of the AO. Thus, the IAO benefits from the advantages of the AO and WOA, and it avoids the limitations of the local search as well as losing solutions diversity through the search process. Moreover, we apply the developed IAO optimization algorithm as a feature selection technique using different benchmark functions. More so, it is tested with extensive experimental comparisons to the traditional AO and WOA algorithms, as well as several well-known optimizers used as feature selection techniques, like the particle swarm optimization (PSO), differential evaluation (DE), mouth flame optimizer (MFO), firefly algorithm, and genetic algorithm (GA). The outcomes confirmed that the using of the WOA operators has a significant impact on the AO performance. Thus the combined IAO obtained better results compared to other optimizers.

Application of the PI-PD cascade control scheme in AGC of a deregulated power system with PV in the presence of communication delay

In this paper, a cascade PI-PD secondary control scheme is presented in automatic generation control (AGC) application under a deregulated environment. Along with conventional electric power generating units, solar is integrated into one of the areas, which influence the frequency control process of the entire grid. Differential Evaluation (DE) algorithm is used to obtain optimal parameter gains of the PI-PD controller of each area simultaneously to control the frequency oscillations of the system. The centralised area controller concept is adopted to provide this control action. Furthermore, the communication delay effect is also investigated in this paper under a deregulated environment when the power system is integrated with solar and supervised by the PI-PD controller to minimise the frequency oscillations.

Variable Neighborhood Strategy Adaptive Search to Solve Parallel-Machine Scheduling to Minimize Energy Consumption While Considering Job Priority and Control Makespan

Environmental concerns and rising energy prices put great pressure on the manufacturing industry to reduce pollution and save energy. Electricity is one of the main machinery energy sources in a plant; thus, reducing energy consumption both saves energy costs and protects our planet. This paper proposes the novel method called variable neighborhood strategy adaptive search (VaNSAS) in order to minimize energy consumption while also considering job priority and makespan control for parallel-machine scheduling problems. The newly presented neighborhood strategies of (1) solution destroy and repair (SDR), (2) track-transition method (TTM), and (3) multiplier factor (MF) were proposed and tested against the original differential evaluation (DE), current practice procedure (CU), SDR, TTM, and MF for three groups of test instances, namely small, medium, and large. Experimental results revealed that VaNSAS outperformed DE, CU, SDR, TTM, and MF, as it could find the optimal solution and the mathematical model in the small test instance, while the DE could only find 25%, and the others could not. In the remaining test instances, VaNSAS performed 16.35–19.55% better than the best solution obtained from Lingo, followed by DE, CU, SDR, TTM, and MF, which performed 7.89–14.59% better. Unfortunately, the CU failed to improve the solution and had worse performance than that of Lingo, including all proposed methods.

Impact of Distributed Generation on a Distribution Network Voltage Sags in Baghdad City

Voltage sags are considered as one of the most detrimental power quality (PQ) disturbance due to their costly influence on sensitive loads. This paper investigates the voltage sag mitigation in distribution network following the occurrence of a fault. Two software are used in this work; the 1st is MATLAB R2017a for implementation of the Differential Evaluation (DE) algorithm to find the optimal location and size DG and while the 2nd software is CYME 7.1 for the distribution system modelling and analysis. The effectiveness of the proposed method is tested by implementing it on IEEE 33-bus system, and then it is applied to Al-Masbh distribution network in Baghdad city as a case study. The paper aims to enhance voltage profile, power loss reduction, and relieve distribution lines overloading, by optimal placement of distributed generation (DG). The results indicate the efficiency of the proposed method comparing with Real Coded Genetic Algorithm (RCGA).

Exploitation whale optimization based optimal offloading approach and topology optimization in a mobile ad hoc cloud environment

Widespread availability of network technologies, mobile user request is increased day by day. The larger amount of energy utilization and resource sufficiency of cloud computing is to create the maximum capacity of exploration and exploitation as troublesome. In this paper, we proposed the formation of mobile user behavior based topology and its optimization. During the offloading process, the minimization of response time and the energy consumption is the major goal of this paper. The topology node formations are performed via improved text rank algorithm (ITRA) and neural network (NN) classifiers with Euclidian distance. In this paper, we introduced an effective optimization algorithm of the exploitation whale optimization algorithm (EWOA) and it is the combination of differential evaluation (DE) and whale optimization algorithms (WOA). The offloading process of proposed EWOA produces an optimal outcome of minimized energy consumption and response time. The implementation works of the proposed EWOA are carried out in the VMware platform. The performance of the proposed method is evaluated using different size puzzles, face detection applications, and state-of-art methods. Ultimately, our proposed method produces optimal accuracy and convergence speed with the minimized offloading process.

Adaptive-Hybrid Harmony Search Algorithm for Multi-Constrained Optimum Eco-Design of Reinforced Concrete Retaining Walls

In the optimum design of reinforced concrete (RC) structural members, the robustness of the employed method is important as well as solving the optimization problem. In some cases where the algorithm parameters are defined as non-effective values, local-optimum solutions may prevail over the existing global optimum results. Any metaheuristic algorithm can be effective to solve the optimization problem but must give the same results for several runs. Due to the randomization nature of these algorithms, the performance may vary with respect to time. The essential and novel work done in this study is the comparative investigation of 10 different metaheuristic algorithms and two modifications of harmony search (HS) algorithm on the optimum cost design of RC retaining walls constrained with geotechnical and structural state limits. The employed algorithms include classical ones (genetic algorithm (GA), differential evaluation (DE), and particle swarm optimization (PSO)), proved ones on structural engineering applications (harmony search, artificial bee colony, firefly algorithm), and recent algorithms (teaching–learning-based optimization (TLBO), flower pollination algorithm (FPA), grey wolf optimization, Jaya algorithm (JA)). The modifications of HS include adaptive HS (AHS) concerning the automatic change of algorithm parameters and hybridization of AHS with JA that is developed for the investigated problem. According to the numerical investigations, recent algorithms such as TLBO, FPA, and JA are generally the best at finding the optimum values with less deviation than the others. The adaptive-hybrid HS proposed in this study is also competitive with these algorithms, while it can reach the best solution by using a lower population number which can lead to timesaving in the optimization process. By the minimization of material used in construction via best optimization, sustainable structures that support multiple types of constraints are provided.