Control Parameters Of Algorithm Research Articles

Population diversity control based differential evolution algorithm using fuzzy system for noisy multi-objective optimization problems

The objective measurements of the real-world optimization problems are mostly subject to noise which occurs due to several reasons like human measurement or environmental factors. The performance of the optimization algorithm gets affected if the effect of noise is higher than the negligible limit. The previous noise handling optimization algorithms use a large population size or multiple sampling at same region which increases the total count of function evaluations, and few methods work for a particular problem type. To address the above challenges, a Differential Evolution based Noise handling Optimization algorithm (NDE) to solve and optimize noisy bi-objective optimization problems is proposed. NDE is a Differential Evolution (DE) based optimization algorithm where the strategies for trial vector generation and the control parameters of DE algorithm are self-adapted using fuzzy inference system to improve the population diversity along the evolution process. In NDE, explicit averaging based method for denoising is used when the noise level is higher than negligible limit. Extending noise handling method enhances the performance of the optimization algorithm in solving real world optimization problems. To improve the convergence characteristics of the proposed algorithm, a restricted local search procedure is proposed. The performance of NDE algorithm is experimented using DTLZ and WFG problems, which are benchmark bi-objective optimization problems. The obtained results are compared with other SOTA algorithm using modified Inverted Generational Distance and Hypervolume performance metrics, from which it is confirmed that the proposed NDE algorithm is better in solving noisy bi-objective problems when compared to the other methods. To further strengthen the claim, statistical tests are conducted using the Wilcoxon and Friedman rank tests, and the proposed NDE algorithm shows significance over the other algorithms rejecting the null hypothesis.

Combined deep-learning optimization predictive models for determining carbon dioxide solubility in ionic liquids

This study explores the development of predictive models for carbon dioxide (CO2) solubility in ionic liquids based on a compiled dataset of 10,116 experimentally measured data points involving four input variables: pressure (P), temperature (T), cation type, and anion type. The deep-learning (DL) predictive models evaluated are standalone and hybrid versions of convolutional neural network (CNN) and long short-term memory (LSTM) algorithms with cuckoo optimization algorithm (COA) and gradient-based optimization (GBO). The laboratory-measured data was separated into training and test categories, and each category was normalized separately to improve the performance of the deep learning algorithms. The Mahalanobis distance-based quantile method was utilized to identify any outliers in the training data. Once identified, the outlier data points were eliminated from the training dataset. The control parameters of the deep learning algorithms were optimized using COA to enhance their efficiency, and the algorithms were hybridized with optimization algorithms to further improve their performance. The resulting models were analyzed to assess their accuracy, degree of overfitting, and the importance of input features. The study found that using 80% of the data for training and 20% for testing results in more accurate and generalizable models. Using the outlier detection method on the training data led to 307 data points being eliminated as outliers. Developing CO2-solubility predictive model showed that, the CNNCOA model had the lowest RMSE and highest R2 among the developed models, indicating high generalizability for data unseen by the trained model. The analysis revealed that using optimization algorithms increased the CO2-solubility prediction performance of DL algorithms and reduced overfitting. T and cation type were the most and least important input features, respectively. Simultaneous changes in cation and anion type on CO2-solubility predictions displayed no systematic pattern. For increases in T, CO2 solubility typically decreased, whereas for increases in P CO2 solubility always increased but at variable rates. The results of this study can be used to develop accurate and generalizable CO2-solubility predictive models for various applications.

Hunger Games Search for the elucidation of gravity anomalies with application to geothermal energy investigations and volcanic activity studies

Abstract Recent metaheuristic approaches are extensively and intensively being implemented to the interpretation of gravity anomalies due to their superior advantages. We emphasize the application of Hunger Games Search (HGS), a newly established metaheuristic inspired by hunger-driven instincts and behavioral choices of animals, to elucidate gravity data for geothermal energy exploration and volcanic activity study. After recognizing the modal features of the objective function tailored and tuning the algorithm control parameters involved, HGS has been trial-tested on simulated data sets of different scenarios and finally experienced in two field cases from India and Japan. Notably, a second moving average strategy has been successfully integrated into the objective function to eradicate the regional component from observed responses. Post-inversion uncertainty appraisal tests have been further implemented to comprehend the reliability of solutions obtained. The solutions retrieved by HGS have been unbiasedly compared in terms of convergence rate, accuracy, stability, and robustness with the solutions of the commonly used particle swarm optimization algorithm. Based on the results accessed, the theoretical and field cases presented could be recuperated more precisely, stably, robustly, and coherently with the available geophysical, geological, and borehole verification, as HGS is able to better explore the model space without compromising its capability to efficiently approach the global minimum. This novel global optimization method can thus be considered as a promising tool in geothermal energy investigations and the study of volcanic activities.

Effect of Variable-Nozzle-Turbocharger-Coupled Exhaust Gas Recirculation on Natural Gas Engine Emissions and Collaborative Optimization

Equivalent combustion natural gas engines typically utilize exhaust gas recirculation (EGR) systems to tackle their high thermal burden and NOx emissions. Variable nozzle turbochargers (VNT) can increase the engine intake and EGR rate simultaneously, resulting in NOx reduction while ensuring robust power performance. Using a VNT along with engine bench testing, the impact of VNT- and EGR-coordinated control on the performance and emissions of equivalent combustion natural gas engines was investigated under different operating conditions. Subsequently, multi-objective optimization was performed using a support vector machine. The results demonstrated that the use of VNTs in equivalent combustion natural gas engines could bolster the capacity to introduce EGR under several operative conditions and extend the scope of EGR regulation, thereby decreasing the engine’s thermal burden, improving fuel efficiency, and curbing emissions. Owing to the implementation of a multi-objective optimization method based on a support vector regression model and NSGA-II genetic algorithm, VNT and EGR control parameters could be optimized to slightly improve the economy and significantly reduce NOx emissions while maintaining the original engine power performance. At 20 operating points optimized for validation, brake-specific fuel consumption (BSFC) and NOx decreased by 0.94% and 47.0%, respectively, and CH4 increased by 3.7%, on average.

An Improved Interference Mitigation Scheme for Femtocell Networks

Femtocell networks have gained significant attention in recent years due to their ability to improve indoor wireless coverage and capacity. However, they are susceptible to interference from neighboring cells, which significantly degrade their performance and quality of service to the end-user. The study introduced an adaptive power control technique that causes the power level of the femtocell base station to adjust dynamically to reduce the interference caused by adjacent femtocell networks, thereby adjusting the system bandwidth, carrier frequency, path loss and femtocell Base Station (BS) transmission power based on the request made by the users. The interference mitigation scheme was optimized by tuning the parameters of the power control algorithm, such as the transmit power levels and the interference thresholds. The algorithm was simulated with MATLAB simulation tools. Throughput, Signal to Interference Noise Ratio (SINR), coverage enhancement and user satisfaction were used as measurement metrics for the model using descriptive statistics. The results showed that the average performance obtained for the throughput increased by 25.88% from 3.40 X 1010 (bps) to 4.28 X 1010 (bps), and the SINR increased by 35.29% from 1.70 X 109 dB to 2.30 X109 dB for the benchmarked power control and improved power control techniques respectively. Coverage enhancement and user satisfaction were nearly 0, which indicates that the technique was of average performance. This study concluded that the new scheme enhances wider coverage to reach more users and mitigate interference while maintaining a higher level of femtocell user satisfaction.

Low-Cost Plant-Protection Unmanned Ground Vehicle System for Variable Weeding Using Machine Vision.

This study presents a machine vision-based variable weeding system for plant- protection unmanned ground vehicles (UGVs) to address the issues of pesticide waste and environmental pollution that are readily caused by traditional spraying agricultural machinery. The system utilizes fuzzy rules to achieve adaptive modification of the Kp, Ki, and Kd adjustment parameters of the PID control algorithm and combines them with an interleaved period PWM controller to reduce the impact of nonlinear variations in water pressure on the performance of the system, and to improve the stability and control accuracy of the system. After testing various image threshold segmentation and image graying algorithms, the normalized super green algorithm (2G-R-B) and the fast iterative threshold segmentation method were adopted as the best combination. This combination effectively distinguished between the vegetation and the background, and thus improved the accuracy of the pixel extraction algorithm for vegetation distribution. The results of orthogonal testing by selected four representative spraying duty cycles-25%, 50%, 75%, and 100%-showed that the pressure variation was less than 0.05 MPa, the average spraying error was less than 2%, and the highest error was less than 5% throughout the test. Finally, the performance of the system was comprehensively evaluated through field trials. The evaluation showed that the system was able to adjust the corresponding spraying volume in real time according to the vegetation distribution under the decision-making based on machine vision algorithms, which proved the low cost and effectiveness of the designed variable weed control system.

A new cooperative control solution of subway BAS: an improved fuzzy PID control algorithm

The building automation system (BAS) of a subway is the core component of monitoring and managing urban rail transit systems. For the current problems such as low control efficiency, insufficient accuracy, and poor stability of metro BAS, this article proposes a cooperative control framework based on an improved fuzzy proportional-integral-derivative (PID) algorithm. Firstly, the concept of an integrated supervisory control system (ISCS) for subways is introduced by summarizing the previously implemented engineering construction and combining it with advanced automation technology. The system’s overall design under the ISCS framework is also improved by integrating it with the fire alarm system (FAS) with the BAS as the core unit of the reliance. Then, an improved seeker optimization algorithm (ISOA) is employed to optimize the parameters of the fuzzy PID control algorithm to achieve a coordinated control of the system based on considering the time lag problem. Finally, the accuracy, efficiency, and stability of the coordinated control response of the BAS under the ISCS framework are tested experimentally. The results suggest that the proposed cooperative control solution of BAS employing the improved fuzzy PID algorithm has good control accuracy and response efficiency and can also ensure the BAS’s higher stability in the coordinated control process, which thus greatly improves the automation level of the subway and provides a safer and more reliable high-performance for the ISCS of the subway in the urban rail transportation industry.

Digital control of the synchronous modes of the two-rotor vibration set-up

The work is devoted to study of the effectiveness of the algorithm for controlling the synchronization of the rotors of a vibration unit (VU) with the digital implementation of the specified algorithm. A computer study of the dynamics of a two-rotor VU with a digital implementation of the control algorithm for rotor synchronization is carried out in the MATLAB environment. As a result the permissible sampling interval for calculating discrete control values using a zero-order extrapolator circuit was estimated. Also, using modeling, a comparative analysis of the efficiency of synchronization control in digital and analog implementation of the algorithm was carried out. It is shown that with an increase in the specified operating speed of the rotors, determined by the value of the energy H^∗ specified in the algorithm, the permissible value of the sampling interval decreases significantly. Comparison of the VU dynamics with the same values of the parameters of the synchronization control algorithm shows that the time of synchronization and the transient process in both cases are almost the same.

Joint scheduling of parallel machines and AGVs with sequence-dependent setup times in a matrix workshop

Transportation systems are often ignored in traditional parallel-machine scheduling problems (PMSP). This study addresses the problem of joint scheduling of parallel machines and AGVs (PMSP-AGV) with sequence-dependent setup times in a matrix manufacturing workshop. The goal is to minimize the makespan from the departure of AGVs to completion of the last job. To achieve this goal, a mixed-integer linear programming model (MILP) is established. An efficient discrete artificial bee colony (DABC) algorithm was proposed with improvement strategies, including a two-stage heuristic for initializing the population, six effective neighborhood operators to explore neighborhood solutions, new algorithm control parameters for deep local exploitation, and a multiple-insertion (MI) method used for re-initialization in the scout bee phase. Many instances from a real factory were used as test benchmarks. Comprehensive analysis of the experimental results showed that the proposed DABC algorithm was significantly superior to the four metaheuristics in the literature.

Совместная оптимизация конструктивных параметров индуктора и алгоритмов управления процессом нагрева под поверхностную закалку

The paper considers an approach for solving the joint optimization problem of design solutions and modes 
 of inductor functioning in the process of heating steel parts of L-shaped form for surface hardening. The first stage solves the problem of optimizing the design of the coils of the inductor and the parameters of the power supply control algorithm, ensuring the maximum uniform heating of the quenched layer to a given temperature above the Curie point in 
 a fixed time. In the second step, a phase limitation is considered, which is a technological requirement to ensure, throughout the heating stage, a temperature not exceeding the limit value. In case of violation of the specified limit, the problem of finding the control algorithm on a special interval within which the maximum temperature in the hardened layer exceeded the maximum permissible value is solved. In the third step, the problem of joint optimization is solved with the found control algorithm at a specific interval of motion on the phase restriction. The described steps can be repeated if necessary. There is presented a test example of the application of the developed strategy for the considered class of technology induction heating for the surface hardening of metal blanks with an angle zone. For the numerical solution in the MATLAB application package developed a software complex, in which integrated two-dimensional nonlinear problem-oriented model of the induction heating process, developed in ANSYS Mechanical APDL. The algorithm for solving the joint optimization problem is based on an alternate method of parametric optimization of systems with distributed parameters. Developed strategy tested on a verified model of interconnected electromagnetic and temperature fields, it is possible to obtain a uniform temperature distribution along the boundary of the hardened layer of steel semi-finished products of complex geometric shape without local overheating throughout the heating stage.

Design of Intelligent Neuro-Supervised Networks for Brain Electrical Activity Rhythms of Parkinson's Disease Model.

The objective of this paper is to present a novel design of intelligent neuro-supervised networks (INSNs) in order to study the dynamics of a mathematical model for Parkinson's disease illness (PDI), governed with three differential classes to represent the rhythms of brain electrical activity measurements at different locations in the cerebral cortex. The proposed INSNs are constructed by exploiting the knacks of multilayer structure neural networks back-propagated with the Levenberg-Marquardt (LM) and Bayesian regularization (BR) optimization approaches. The reference data for the grids of input and the target samples of INSNs were formulated with a reliable numerical solver via the Adams method for sundry scenarios of PDI models by way of variation of sensor locations in order to measure the impact of the rhythms of brain electrical activity. The designed INSNs for both backpropagation procedures were implemented on created datasets segmented arbitrarily into training, testing, and validation samples by optimization of mean squared error based fitness function. Comparison of outcomes on the basis of exhaustive simulations of proposed INSNs via both LM and BR methodologies was conducted with reference solutions of PDI models by means of learning curves on MSE, adaptive control parameters of algorithms, absolute error, histogram error plots, and regression index. The outcomes endorse the efficacy of both INSNs solvers for different scenarios in PDI models, but the accuracy of the BR-based method is relatively superior, albeit at the cost of slightly more computations.

Enhancing Efficiency of Grid-Connected Solar Photovoltaic System with Particle Swarm Optimization & Long Short-Term Memory Hybrid Technique

Maximum Power Point Tracking (MPPT) is a technique used in photovoltaic (PV) systems to maximize the power output from the solar panel by constantly tracking and adjusting the optimal operating point. To achieve this, various algorithms have been developed, with Particle Swarm Optimization (PSO) being a widely used method. By adjusting the control system’s parameters, PSO can determine the optimal operating point of the solar panel and improve its overall performance. PSO employs swarm intelligence by simulating the behavior of particles to find the best solution for a given problem. Long Short-Term Memory (LSTM) belongs to the family of Recurrent Neural Networks (RNN) in machine learning and is designed to address the limitations of traditional RNNs in capturing long-term dependencies that exist in sequential data. The combination of PSO and LSTM techniques can result in an efficient MPPT algorithm that leverages the benefits of both. PSO is utilized to optimize the control parameters of the MPPT algorithm, while LSTM is used to predict the solar panel’s power output based on historical data. Consequently, this integration can lead to an accurate and efficient MPPT algorithm that can effectively track the solar panel’s maximum power point. In this research article, an effort has been made to control the duty cycle of the converter by suitably controlling the system gain. A Matlab-based Simulink model in conjunction with Python programming has been used to make the system more robust.

Insight into evolutionary optimization approach of batch and fed-batch fermenters for lactic acid production

Differential evolution (DE) algorithm and genetic algorithm (GA) are used in this study to estimate a set of kinetic parameters for Arabic date juice-based lactic acid production via batch and fed-batch mode of fermentation. Different feeding approaches like feed-forward control, exponential-, and modified exponential- feed are employed to obtain optimum kinetic parameters. The global optimum sets of kinetic parameters for both fermentation methods are found by minimizing the least square error between the experimental data and the simulated model results. In both batch and fed-batch fermentation methods (including different feeding strategies) the DE algorithm resulted in either the least value of the objective function or the least value of the sum of the square of residual errors between the experimental and model-predicted values for biomass growth (X), substrate consumption (S), and product formation (P). Six different strategies of the DE algorithm are used and their performance is compared for exponential feeding fed-batch fermenter. For exponential feeding fed-batch fermenter best suitable DE strategies were found to be best/1/bin and current to best/1/bin based on algorithm control parameters analysis. This manuscript highlights the limitations and improvements in the performance of individual algorithms on the given biochemical fermenters.

Reinforcement-Learning-Based Multi-Objective Differential Evolution Algorithm for Large-Scale Combined Heat and Power Economic Emission Dispatch

As social and environmental issues become increasingly serious, both fuel costs and environmental impacts should be considered in the cogeneration process. In recent years, combined heat and power economic emission dispatch (CHPEED) has become a crucial optimization problem in power system management. In this paper, a novel reinforcement-learning-based multi-objective differential evolution (RLMODE) algorithm is suggested to deal with the CHPEED problem considering large-scale systems. In RLMODE, a Q-learning-based technique is adopted to automatically adjust the control parameters of the multi-objective algorithm. Specifically, the Pareto domination relationship between the offspring solution and the parent solution is used to determine the action reward, and the most-suitable algorithm parameter values for the environment model are adjusted through the Q-learning process. The proposed RLMODE was applied to solve four CHPEED problems: 5, 7, 100, and 140 generating units. The simulation results showed that, compared with four well-established multi-objective algorithms, the RLMODE algorithm achieved the smallest cost and smallest emission values for all four CHPEED problems. In addition, the RLMODE algorithm acquired better Pareto-optimal frontiers in terms of convergence and diversity. The superiority of RLMODE was particularly significant for two large-scale CHPEED problems.

A Fast Hyperchaotic Image Encryption Scheme

In this paper, we propose a fast hyperchaotic image encryption scheme based on RSVM and step-by-step scrambling-diffusion. In this scheme, we firstly propose a new algorithm named ring shrinkage with variable modulo (RSVM), which can randomly scramble the elements in a one-dimensional array, which are composed of the row numbers or column numbers of the pixel matrix to be encrypted. Before encryption, we use RSVM algorithm to generate two random one-dimensional arrays of pixel matrix (i.e. row array [Formula: see text] and column array [Formula: see text]), and each element in the [Formula: see text] represents the row/column number in the pixel matrix. Then the rows/columns of the pixel matrix are scrambled-diffused step-by-step according to the row/column numbers in the [Formula: see text]. The initial control parameters of RSVM algorithm are controlled by SHA-256 of plaintext pixels, and RSVM algorithm controls the step-by-step scrambling-diffusion process of pixel matrix, rows and columns, so the small changes of plaintext pixels will lead to great differences in ciphertext images. In addition, the overall time complexity of the image encryption scheme is only [Formula: see text], which can greatly reduce the time cost. Finally, the experimental results and extensive security analysis prove the efficiency and feasibility of this image encryption method.

Hybrid Alternated Inertial Projection and Contraction Algorithm for Solving Bilevel Variational Inequality Problems

This paper considers an alternated inertial-type extrapolation algorithm for solving bilevel pseudomonotone variational inequality problem in the framework of real Hilbert spaces with split variational inequality and fixed-point constraints of demimetric mapping. The algorithm which involves alternated inertial uses self-adjustment stepsize condition that depends solely on the information from previous iterative step. The bilevel problem considered in the work consists of upper-level problem with an underlying operator which is pseudomonotone, while the lower problem is associated with strongly monotone mapping and the fixed-point constraint of demimetric mapping. Our algorithm is anchored on modified projection and contraction techniques, fused with alternated inertial and relaxation. Under some suitable conditions on the algorithm control parameters, we obtain a strong convergence result of the proposed method without the prior knowledge of the operator norm or the coefficients of the underlying operators within the scope of real Hilbert spaces. Finally, some numerical examples are presented to illustrate the gain of our method in comparison to some related algorithms in the literature.

An Improved Differential Evolution Adaptive Fuzzy PID Control Method for Gravity Measurement Stable Platform.

In the platform gravimeter, the stabilization accuracy of the gravimetric stabilization platform is crucial to improve the accuracy of gravity value measurements due to its uncertainties, such as mechanical friction, inter-device coupling interference, and nonlinear disturbances. These cause fluctuations in the gravimetric stabilization platform system parameters and present nonlinear characteristics. To resolve the impact of the above problems on the control performance of the stabilization platform, an improved differential evolutionary adaptive fuzzy PID control (IDEAFC) algorithm is proposed. The proposed enhanced differential evolution algorithm is used to optimize the initial control parameters of the system adaptive fuzzy PID control algorithm to achieve accurate online adjustments of the gravimetric stabilization platform's control parameters when it is subject to external disturbances or state changes and attain a high level of stabilization accuracy. The results of simulation tests, static stability experiments, and swaying experiments on the platform under laboratory conditions, as well as on-board experiments and shipboard experiments, all show that the improved differential evolution adaptive fuzzy PID control algorithm has a higher stability accuracy compared with the conventional control PID algorithm and traditional fuzzy control algorithm, proving the superiority, availability, and effectiveness of the algorithm.

A fast switching strategy for DVR based on current control algorithm

Dynamic voltage restorer (DVR) needs to turn off the anti-parallel bypass thyristor (SCR) at the output terminal when dealing with voltage sag phenomenon. When applying traditional methods to force the thyristor off, it is essential to determine the direction of thyristor current according to the sensor sampling information, which has the risk of misjudgment under actual conditions. A novel fast switching strategy (FSS) for DVR is proposed in this paper. DVR-FSS uses the current control algorithm to force off the bypass SCR, which can realize the smooth transition from bypass state to compensation state. Firstly, the basic working principle of the DVR-FSS is introduced based on which the FSS operating modes are analyzed in detail. And the mathematical model of DVR-FSS in key modes is established to analyze the influence of FSS control algorithm parameters on the dynamic or steady performance of DVR. Secondly, the modeling and quantitative analysis of the fundamental voltage drop introduced by DVR in the steady-state mode are used to explain that the impact of FSS on the primary side load supply voltage is within the acceptable range. Finally, the correctness and feasibility of the proposed FSS are fully verified by experiments, which indicate that FSS has strong robustness at the same time.

Zero-Drift Error Compensation Method for Electronic Balance Based on Adaptive Filter With Autostep LMS

This article presents a new approach of compensating the zero-drift error of electronic balance based on automatic step adjustment and least mean square method (Autostep-LMS), and the detailed algorithm of Autostep-LMS is given. Unlike the classical variable step-size LMS algorithms, such as Aboulnasr-LMS algorithm, Versoria-function-LMS algorithm, and Gradient-LMS algorithm, the step control parameters of Autostep-LMS algorithm are automatically adjusted, which is helpful to reduce the complexity of how to adjust these algorithm parameters. By comparing with these classical variable step-size LMS algorithms, the simulation results show that the Autostep-LMS algorithm has faster convergence speed and smaller steady-state error. This Autostep-LMS algorithm is applied to the electronic balance, which range is 200 g and the resolution is 1 mg, and the experimental results show that the zero-drift error, repeatability error, and nonlinear error of the electronic balance are all less than 5 mg, which is superior to the recommendation of the class II electronic balance.

Availability Capacity Evaluation and Reliability Assessment of Integrated Systems Using Metaheuristic Algorithm

Contemporarily, the development of distributed generations (DGs) technologies is fetching more, and their deployment in power systems is becoming broad and diverse. Consequently, several glitches are found in the recent studies due to the inappropriate/inadequate penetrations. This work aims to improve the reliable operation of the power system employing reliability indices using a metaheuristic-based algorithm before and after DGs penetration with feeder system. The assessment procedure is carried out using MATLAB software and Modified Salp Swarm Algorithm (MSSA) that helps assess the Reliability indices of the proposed integrated IEEE RTS79 system for seven different configurations. This algorithm modifies two control parameters of the actual SSA algorithm and offers a perfect balance between the exploration and exploitation. Further, the effectiveness of the proposed schemes is assessed using various reliability indices. Also, the available capacity of the extended system is computed for the best configuration of the considered system. The results confirm the level of reliable operation of the extended DGs along with the standard RTS system. Specifically, the overall reliability of the system displays superior performance when the tie lines 1 and 2 of the DG connected with buses 9 and 10, respectively. The reliability indices of this case namely SAIFI, SAIDI, CAIDI, ASAI, AUSI, EUE, and AEUE shows enhancement about 12.5%, 4.32%, 7.28%, 1.09%, 4.53%, 12.00%, and 0.19%, respectively. Also, a probability of available capacity at the low voltage bus side is accomplished a good scale about 212.07 times/year.