Wireless Power Transfer (WPT) for electric vehicles is one of the most promising methods that, given its advantages, will drive the deployment of electric vehicles. This paper presents a mathematical ...optimization method applied to the complete design of an LCC-S WPT3 Z1 11 kW system that complies with the SAE J2954 standard (Wireless Power Transfer for Light-Duty Plug-in/Electric Vehicles and Alignment Methodology, 2020). A design method based on three phases is proposed, allowing the complete inductor system, including ferrites shielding and compensation circuit components, to function in any relative primary and secondary position. In Phase 1, a multi-objective NSGA-II algorithm is designed, utilizing three nested genetic algorithms. The goal is simultaneously searching for the local optimum between the primary and secondary systems in three positions. This is achieved by modeling the circuit’s electrical and electromagnetic parameters with equations, enabling an iterative process with reduced computational time. The NSGA-II algorithm yields three scenarios: primary copper volume minimization, secondary copper volume minimization, and a compromise solution that optimizes the total volume. The result is then modeled in Phase 2 using a 3D finite element program that includes ferrite and optimal shielding, obtaining the values of inductances and mutual inductance in the three positions, as well as design data for manufacturing. This result is introduced in Phase 3 to optimize compensation circuit components using a second NSGA-II algorithm with three nested genetic algorithms. Again, three scenarios are obtained based on the desired system behavior and the optimal cost of the components. The result is validated through simulation with Matlab-Simulink and experimentally using a prototype constructed for this purpose.
•Methodology for optimizing WPT systems based on simplifying variables.•NSGA-II with three nested GAs for the optimal design of SAE J2954-compliant WPTs.•Equation-based modeling and FEM for inductances, minimizing calculation time.•Overall price optimization, including all passive components of the topology.•Global design method that minimizes development time and computational cost.
Very recently, the first mathematical runtime analyses of the multi-objective evolutionary optimizer NSGA-II have been conducted. We continue this line of research with a first runtime analysis of ...this algorithm on a benchmark problem consisting of multimodal objectives. We prove that if the population size N is at least four times the size of the Pareto front, then the NSGA-II with four standard ways to select parents, bit-wise mutation, and crossover with rate less than one, optimizes the OneJumpZeroJump benchmark with jump size 2≤k≤n/4 in time O(Nnk). When using fast mutation instead of bit-wise mutation this guarantee improves by a factor of kΩ(k). Overall, this work shows that the NSGA-II copes with the local optima of the OneJumpZeroJump problem at least as well as the global SEMO algorithm.
Dynamic cerebral autoregulation (dCA) has been addressed through different approaches for discriminating between normal and impaired conditions based on spontaneous fluctuations in arterial blood ...pressure (ABP) and cerebral blood flow (CF). This work presents a novel multi-objective optimisation (MO) approach for finding good configurations of a cerebrovascular resistance-compliance model.
Data from twenty-nine subjects under normo and hypercapnic (5% CO2 in air) conditions was used. Cerebrovascular resistance and vessel compliance models with ABP as input and CF velocity as output were fitted using a MO approach, considering fitting Pearson’s correlation and error.
MO approach finds better model configurations than the single-objective (SO) approach, especially for hypercapnic conditions. In addition, the Pareto-optimal front from the multi-objective approach enables new information on dCA, reflecting a higher contribution of myogenic mechanism for explaining dCA impairment.
•Multi-objective optimization-based fractional-order PID controller is designed.•NSGA-II algorithm is augmented with chaotic Logistic and Henon map.•Load disturbance rejection and controller effort ...are minimized as two conflicting objectives.•FOPID controller outperforms the PID controller in suppressing frequency deviation.•Better trade-off is obtained for load-frequency control of power systems with FOPID.
Fractional-order proportional-integral-derivative (FOPID) controllers are designed for load-frequency control (LFC) of two interconnected power systems. Conflicting time-domain design objectives are considered in a multi-objective optimization (MOO)-based design framework to design the gains and the fractional differ-integral orders of the FOPID controllers in the two areas. Here, we explore the effect of augmenting two different chaotic maps along with the uniform random number generator (RNG) in the popular MOO algorithm—the Non-dominated Sorting Genetic Algorithm-II (NSGA-II). Different measures of quality for MOO, e.g. hypervolume indicator, moment of inertia-based diversity metric, total Pareto spread, spacing metric, are adopted to select the best set of controller parameters from multiple runs of all the NSGA-II variants (i.e. nominal and chaotic versions). The chaotic versions of the NSGA-II algorithm are compared with the standard NSGA-II in terms of solution quality and computational time. In addition, the Pareto optimal fronts showing the trade-off between the two conflicting time domain design objectives are compared to show the advantage of using the FOPID controller over that with simple PID controller. The nature of fast/slow and high/low noise amplification effects of the FOPID structure or the four quadrant operation in the two inter-connected areas of the power system is also explored. A fuzzy logic-based method has been adopted next to select the best compromise solution from the best Pareto fronts corresponding to each MOO comparison criteria. The time-domain system responses are shown for the fuzzy best compromise solutions under nominal operating conditions. Comparative analysis on the merits and de-merits of each controller structure is reported then. A robustness analysis is also done for the PID and the FOPID controllers.
•Thermodynamic and thermo-economic modeling of irreversible regenerative closed Brayton cycle is performed.•The latter is achieved using NSGA algorithm and thermodynamic and thermo-economic ...analysis.•Well known decision makers are carried out to specify optimum values of outputs.
This paper goes through a sophisticated ecological function for irreversible regenerative closed Brayton cycle. Moreover, aforementioned irreversible regenerative closed Brayton cycle is optimized by implementing ecological function. With the aim of the first and second laws of thermodynamics, an equivalent system is initially specified. Developed multi objective evolutionary approaches (MOEAs) on the basis of NSGA-II method is implemented throughout this work. To accomplish the above mentioned target of this paper, three objective functions which includes the power output (P), the ecological function (E) and thermoeconomic criterion (F) are perceived in optimization process simultaneously. Three aforementioned objective functions are maximized at the same time.
•A novel fault diagnosis scheme was proposed based on optimization methods.•Nonstationary and nonlinear multivariate chemical processes were analyzed.•NSGAII was utilized for feature selection and ...t-SNE method was used as feature extraction and visualization method.•DBSCAN, k-means, CURE methods were utilized for non- automatic unsupervised learning investigation.•GA, ABC, DE, HS, and PSO, in combination with DB and CS clustering measures were utilized as automatic unsupervised learning investigation.•The proposed method performed well for fault detection and diagnosis of chemical processes.
Fault detection and diagnosis (FDD) is crucial for ensuring process safety and product quality in the chemical industry. Despite the large amounts of process data recorded and stored in chemical plants, most of them are not well-labeled, and their conditions are not adequately specified. In this study, an optimized data-driven FDD model was developed for a chemical process based on automatic clustering algorithms. Due to data preprocessing importance, feature selection was performed by a non-dominated sorting genetic algorithm (NSGAII) based on k-means clustering. The optimal subset of features is selected by comparing clustering results for each subset. The performance of the proposed feature selection method was compared with the Fisher discriminant ratio (FDR), and XGBoost methods. The t-distributed stochastic neighbor embedding (t-SNE), Isomap, and KPCA dimension reduction methods were also employed for feature extraction. Finally, automatic clustering was performed based on metaheuristic algorithms for fault detection and diagnosis. Results were compared with non-automatic clustering methods. The performance of the proposed method was evaluated by examining the Tennessee Eastman and four water tank processes as case studies. The results showed that the proposed method is reliable and capable of online and offline chemical process fault detection and diagnosis. As a result, the findings of this study can be used to stabilize the operation of chemical processes.
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Calibration of hydrological models for watersheds is critical considering the hydrological processes involved. The Soil and Water Assessment Tool (SWAT) is one such popular model and requires proper ...calibration, without which models have difficulty in proper simulation of runoff. The present study aims to utilize multi-objective calibration framework using Non-Dominated Genetic Algorithm- II (NSGA-II) and SWAT-Calibration Uncertainty Procedures (SWATCUP) for calibration. The study is conducted on Musi river basin located in India (10,000 Sq km) for seven years from 2013-2016. It includes an initial warm-up period of three years, the calibration period from 2015-2016, and validation period from 2014-2015. NSGA-II aims to optimize the multiple objective functions i.e. Nash Sutcliffe Efficiency (NSE) and Percentage Bias (PBias). The Monthly simulations results are expressed in terms of statistical parameters NSE, R
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and PBias for calibration and validation period. The results indicate satisfactory performance. Further, NSGA-II results are compared with SWATCUP (Sequential Uncertainty Fitting ver.2 (SUFI-2). We find NSGA-II performance is better than SWATCUP. The sensitive analysis indicates that CN2, GW_DELAY, GW_REVAP, ALPHA_BF, RCHRG_DP, and CH_K2 are very sensitive whereas SURLAG, ESCO, SLSUBBS, HRU_SLP are observed to be least sensitive.
In order to balance the maximum mixing efficiency and minimum energy consumption of stirred tanks, this study proposes a four-stage optimization framework, integrating all: CFD model, ANN ...data-prediction model, multi-objective optimization model, and multi-criteria decision making model. With the stirred tank reactor as the modeling and simulation context, a data-prediction model, GA-GABP, is developed firstly. Second, an optimization model is established targeting energy consumption, fluid mixing degree, and suspension uniformity: the predictions of GA-GABP are optimized using the NSGA II, and finally, based on the Pareto front, weights are determined using the entropy weighting method, and the TOPSIS algorithm is employed to decide the final optimization scheme. Compared to the base case, the optimized scheme Opt1 shows a 52.49% reduction in energy consumption, a 1.35% increase in fluid mixing degree, and a 72.31% improvement in suspension uniformity. This demonstrates the framework's effectiveness in balancing multiple conflicting objectives.
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•A four-stage optimization framework was proposed.•The key parameters affecting the performance were studied and optimized.•Two-layer genetic algorithm was used to obtain the best hidden layer combination.•Influence weights of key parameters were studied with stochastic forest algorithm.•The optimal structural parameters were obtained.
An increasing amount of malicious code causes harm on the internet by threatening user privacy as one of the primary sources of network security vulnerabilities. The detection of malicious code is ...becoming increasingly crucial, and current methods of detection require much improvement. This paper proposes a method to advance the detection of malicious code using convolutional neural networks (CNNs) and intelligence algorithm. The CNNs are used to identify and classify grayscale images converted from executable files of malicious code. Non-dominated Sorting Genetic Algorithm II (NSGA-II) is then employed to deal with the data imbalance of malware families. A series of experiments are designed for malware image data from Vision Research Lab. The experimental results demonstrate that the proposed method is effective, maintaining higher accuracy and less loss.
•A technique for converting a malware binary to an image was introduced.•In this paper, a method based on CNN is used to identify and classify the malicious codes.•An effective data equilibrium approach based on the NSGA-II was designed.•The proposed method was demonstrated through the extensive experiments.
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