Genetic Algorithm Optimization Technique Research Articles

Machine learning approach for the effectual production of a novel esterase and its application in bioremediation of dairy effluent

The present work intends to maximize the production of an industrially important novel esterase from Trichoderma harzianum using both machine learning and statistical approaches and its application in the bioremediation of dairy effluent. Significant medium compounds, such as almond oil, casein, NaH2PO4, and trace elements, were screened from a pool of carbon, nitrogen, oil and surfactant sources along with other micronutrients using a first order statistical design. The optimum value of screened parameters was found using response surface methodology that resulted in the maximum esterase production of 2985.31 U/L. Artificial neural network trained with the experimental response of RSM experiments coupled with genetic algorithm optimization technique has resulted in the maximum esterase production of 3256.73 U/L, which is 150-folds higher as compared to the unoptimized condition and also found to be the higher than the outcome of statistical optimization. Dairy effluent was used for the production of esterase as well as for its bioremediation. At 50% load, the maximum reduction in chemical oxygen demand and the production of esterase were observed to be 46.84% and 34.12 U/L, respectively. The COD reduction of diary effluent was modelled using Michaelis-Menten kinetics and the kinetic parameters, half saturation constant (Km) and maximum reaction rate (rmax) value were evaluated as 286.88 mg/L and 26.04 mg/L/d, respectively.

Using Improved Hybrid Grey Wolf Algorithm Based on Artificial Bee Colony Algorithm Onlooker and Scout Bee Operators for Solving Optimization Problems

Grey Wolf optimization (GWO) is a newly developed stochastic meta-heuristic technique motivated by nature. It shows potential in diverse optimization challenges. It replicates grey wolf hunting behaviour and social hierarchy, exploring the solution space similar to their natural process. The algorithm efficiently explores and converges to the optimal solution. However, a drawback of the standard GWO is its limited exploitation capability due to its exploration-focused iterations. This may hinder finding the optimal solution nearby, leading to lower local convergence rates and degraded solution quality. To address this, the GWO-Employed-Onlooker model suggests incorporating the onlooker and scout bee operators from the artificial bee colony algorithm (ABC) during the position-changing stage of the grey wolves. This enhances exploitation capability, resulting in improved local convergence rates and better solution quality. The proposed method’s performance is evaluated on various optimization functions and compared their convergence rate to standard GWO, Genetic Algorithm (GA), Firefly Algorithm (FA), ABC, and Ant Colony Optimization (ACO) techniques. The results demonstrate that the proposed strategy GWO-Employed-Onlooker is better, indicating that it is valuable in solving optimization problems.

A novel metaheuristic optimization and soft computing techniques for improved hydrological drought forecasting

Drought is one of the costliest natural disasters worldwide and weakens countries economically by causing negative impacts on hydropower and agricultural production. Therefore, it is necessary to create drought risk management plans by monitoring and predicting droughts. Various drought indicators have been developed to monitor droughts. This study aims to forecast Streamflow Drought Index (SDI) values with various novel metaheuristic optimization-based Artificial Neural Network (ANN) and deep learning models to predict 1-month lead-time hydrological droughts on 1, 3, and 12-month time scales in the Konya closed basin, one of the driest basins in Turkey. To achieve this goal, the ANN model was integrated with the Firefly Algorithm (FFA), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) techniques and compared against long short-term memory (LSTM) networks. While establishing the SDI prediction model, lag values exceeding the 95% confidence intervals in the partial autocorrelation function graphs were used. Model performance was evaluated according to scatter matrix, radar, time series, bee swarm graphs, and statistical performance metrics. As a result of the analysis, the PSO-ANN hybrid model with (R2:0.468–0.931) at station 1611 and the FFA-ANN hybrid model with (R2:0.443–0.916) at station 1612 generally have the highest accuracy.

Performance Analysis of Automatic Generation Control for a Multi-Area Interconnected System Using Genetic Algorithm and Particle Swarm Optimization Technique

The primary focus of this paper is to assess an interconnected power system using different optimization techniques. The main purpose is to employ different optimization techniques, including genetic algorithms (GA) and particle swarm optimization (PSO), to systematically enhance the performance of a multi-area or two-area automatic generation control (AGC) system, aiming to optimize the three PID controllers gain values and improve system performance under diverse loading conditions. Two case studies are conducted exploring different loading conditions in the megawatt (MW) range, including increasing load demand and decreasing load demand. The analysis involves four scenarios, covering without any kind of controller, another with solely a proportional integral derivative (PID) controller, a PID controller enhanced through a genetic algorithm (GA), and lastly, a PID controller improved through particle swarm optimization (PSO). The optimization process utilizes the integral time absolute error (ITAE) as the objective function to evaluate the system's performance. The simulation outcomes for ITAE, settling time, overshoot, and undershoot for frequency deviation of area one, area two, and power deviation in the tie-line are compared with previous similar studies to assess the novelty of this work. The article highlights the importance of the multi-area AGC system and the significance of different optimization techniques in improving its performance.

Self fuzzy-genetic approach for boosting photovoltaic efficiency

The generation of power in Photovoltaic systems is reduced when they operate far from their maximum power point. For optimal operation, it is essential to continuously track the maximum power point of the PV solar array. However, identifying the maximum power point is a challenge due to the nonlinear relationship of electrical characteristics of PV panels with external factors. To address this issue, we present a novel design approach for a self-organizing, self-tuning fuzzy logic controller, applied to the problem of maximum power point tracking in photovoltaic systems. We outline the basic structure of the fuzzy logic controller and address the design problems typically associated with conventional trial-and-error schemes. We also discuss the suitability of the genetic algorithm optimization technique for determining and optimizing the fuzzy logic controller design. In our proposed approach, we translate the normalization factors, membership function parameters, and controller policy into bit-strings, which are then processed by the genetic algorithm to find a near-optimal solution. To achieve high dynamic performance, we choose a particular objective function as a performance index. We compare our approach with two variants of the maximum power point algorithm, one based on genetic algorithms and the other based on fuzzy logic, as well as with the methods described in references [34] and [35], in order to evaluate its effectiveness.

Dividing‐wall column for separating wide boiling mixture: Optimal design and servo control strategy

AbstractThe dividing‐wall column (DWC) stands as an energy‐efficient distillation technology designed to efficiently separate ternary systems into their pure components within a single column. Despite its efficiency, integrating two towers into a DWC poses challenges in controllability. Published studies have focused on developing controllers for disturbance rejection but have not addressed the issue of servo control. Addressing this, our study proposes a servo controller problem and a methodical approach to strategically select controlled variables that undergo consecutive set point step changes, aiming to track the purity of key products obtained from the DWC. To achieve this goal, we developed a dynamic model specifically tailored to the DWC. Determining the design parameters involved employing a genetic algorithm optimization technique, minimizing the total annual cost. Subsequently, we implemented three servo parallel proportional‐integral (PI) feedback controllers for three distinct product streams. The fine‐tuning of their controller gain and reset time is carried out by minimizing the integrated square error while adhering to valve opening constraints. Our investigation revealed a limitation: the introduction of simultaneous step changes in all three controlled variables proved unfeasible with the three active PI controllers. As an alternative, our findings recommend prioritizing two variables: the lightest (distillate) and heaviest (bottom) key components, alongside the middle component (side stream) and either the lightest or heaviest key component. This research underscores the complexities of implementing step changes in multiple controlled variables within a DWC, offering insights into optimal control strategies for enhancing the efficiency of this innovative distillation technology.

Radial basis function‐based Pareto optimization of an outer rotor brushless DC motor

AbstractThis paper presents the development of an optimization and modeling method for the objective functions of output power, efficiency and weight of an outer rotor permanent magnet brushless DC (BLDC) motor based on radial basis function (RBF) approximation technique. The proposed RBF‐based Pareto optimization method requires less knowledge about electric/magnetic formulas and can replace conventional optimizations based on these equations with higher accuracy. To apply the proposed optimization method, the initial design should be developed using such equations. Therefore, RBFs are used to model and predict engine behavior. To optimize the objective functions, we used a genetic algorithm optimization technique with nonlinear electric and magnetic constraints to find the Pareto front set. The design obtained by the proposed radial basis function Pareto optimization (RBFPO) method was finally verified by Ansoft Maxwell. The results of optimal design using the RBFPO method have higher output power and efficiency. Also, in addition to the advantage of a favorable accuracy, RBF‐based models are significantly faster than models available in simulation tools.

Multigene genetic programming approach for modelling and optimisation of removal of heavy metals from ash pond water using cyanobacterial-microalgal consortium

ABSTRACT Heavy metals such as Lead(II), Nickel(II), Manganese(II), Cadmium(II), Chromium(VI), and others are leached from coal ash in thermal power plants, contaminating ash pond water and subsequently contaminating groundwater. Phycoremediation using microalgae or cyanobacteria is an emerging technology to remove such heavy metals from ash pond water. The present study aims at development of an accurate data-driven Genetic Programing (GP) approach for modelling of the phycoremediation process for abatement of the above-mentioned heavy metals using a consortium comprising of a cyanobacterium Synechococcus sp. and green algae Chlorella sp. The developed model was used to find a relation between the average percentage removal of metals with all input parameters such as the initial metal concentrations, pH, and days. To maximise metal removal, Genetic Algorithm (GA) optimisation technique was applied to determine optimal values of input parameters. These optimum input parameters are difficult to get through experimentation using the trial and error method. The established modelling and optimisation technique is generic and can be applied to any other experimental study.

Residential Building Design and Optimization in Arid Climates using Multiple Objectives and ANN-GA

Residential buildings in arid regions, such as South Algeria, are responsible for a considerable portion of the total electrical energy consumption, surpassing 60%. To tackle this challenge, this research proposes a novel multi-objective approach aimed at designing typical residential buildings. This approach combines the utilization of a Genetic Algorithm (GA) optimization technique with an Artificial Neural Network (ANN) model, implemented using the Python programming language and integrated with TRNSYS software. The performance of the ANN model is thoroughly evaluated and validated using the TRNSYS software. The integration of Python and TRNSYS facilitates the generation of training datasets for the ANN model. This co-simulation framework provides a powerful tool for accurately estimating energy consumption and life cycle costs (LCC). The results obtained through the TOPSIS-based analysis reveal a remarkable reduction in cooling and heating energy usage, up to 51.43% and 98.97%, respectively, when compared to the basic scenario.

Optimisation of laser welding parameters to maximise the joint strength of laser weld stainless steel joints using a GA for geothermal plants

ABSTRACTLaser beam welding is vital in high-temperature fields such as the aerospace, automobile, and gas turbine manufacturing industries. In this article, parametric studies are carried out on Nd:YAG laser weldments that influence stainless steel 430 L weldment quality. A series of trial runs are organised to investigate input parameters such as peak power, frequency, speed, and time that impact output responses such as yield strength, ultimate tensile strength, shear strength, and elongation simultaneously. Trail runs on martensitic stainless steel AISI 430 L are conducted based on Taguchi experiments, and a regression equation is established. To yield optimum results, the process parameters are optimised by using the genetic algorithm optimisation technique. By conducting experimentation with optimum parameters, the output responses are compared with the optimum results 95% confidence level was obtained. The optimum levels for process variables are identified for quality weld peak power 160 kW, frequency 9 Hz, speed 3.33 mm/ms, and time 2.53 ms. Optical microscopy reveals that refined grains are formed in the weld zone with a grain size of 2.909 µm, and a scanning electron microscopy test is performed to study the morphology of AISI 430 L weldment specimen failure takes place at the interface of the weld metal.

An IoT-fog-cloud consensus-based energy management algorithm of multi-agent smart energy hubs considering packet losses and uncertainty

—As a result of the increasing demand for different energies with technological development, and with the rapid development of energy hubs (EHs), issues such as computation speed, big data and information privacy, robustness, etc. are becoming increasingly vital. Also, the problem of communication packet loss leads to an incompatibility between the generated and consumed energy. In response to the above problems, this paper proposes an Internet of things-Fog-Cloud distributed consensus-based energy management algorithm to optimally solve the energy mismatch problem in an EH environment considering communication information loss and uncertainties of some EH elements. A MATLAB/Simulink-based model of a hypothetical EH system is modified to support real-time communication. Two case studies are applied to explain the performance and effectiveness of the proposed algorithm under different packet loss scenarios. The system's sensitivity was studied depending on the number of iterations and the uncertainties of the renewable energy sources, energy prices, and load demands. Also, a comparison was made between the results obtained by the proposed method and the optimization results using the genetic algorithm and particle swarm optimization techniques. The results prove the applicability and feasibility of the proposed method for the demand management system in EHs. The results show that the proposed algorithm decreases the daily system operating cost from 372.421 to 369.941 $ and the total CO2 emissions cost from 4848.5 to 4103.1 $. In addition, the daily energy mismatch between the generated and consumed values decreases from 579.85 to 0 kWh in the electrical system and from 435.14 to 0.073 kWhth in the heating system. Based on the previous results, the proposed algorithm of EHs contributes significantly to the development of sustainable cities.

Distributed Generation Allocation in Distribution Network for Energy Losses Reduction

Abstract This paper presents a method for distributed generation (DG) allocation in medium voltage (MV) distribution system based on energy loss minimization. The main objective of the research is to design, implement and test a DG allocation (siting and sizing) method and to investigate how optimal DG allocation influence the operational parameters of the system from the Distribution System Operator (DSO) perspective. The problem is formulated as a single objective optimization problem solved by using both genetic algorithm and particle swarm optimization techniques. Model of a realistic Electric Power Distribution System (EPDS) and IEEE 37-bus EPDS are used as test systems. The results confirm that proposed algorithms can be used for practical DG allocation. The research presented contributes to the field as it provides a DG allocation method for energy loss reduction performed on a EPDS which can be applied in realistic planning and regulatory situations using open-source software.

Strength design optimization of sandwich composite structures under heavy dynamic loads

Recent researches have witnessed an increased interest in the Rapid Runway Repair (RRR) methods to rehabilitate damages that may be caused by different incidents, such as: natural disasters of earthquakes, floods or man-made vandalism in civil wars. RRR is a strategic process for airport operations for civil and peace-making missions. The current RRR techniques like precast concrete slabs, metal mats, and fiberglass mats have different pros and cons. The current study numerically investigates CFRP sandwich composite structure for RRR usage, where its strength is maximized by design optimization to reach the possible carrying aircraft wheel capacity and safety factors. The proposed composite structure is advantageous of no corrosion, low erection time, high capacity-to-weight ratio, same finish of runway surface and repaired area, and can be applied over spots of unlevelled or inadequate bearing capacity of 60 cm diameter. The strength of the basic design of composite sandwich structure is first assessed to its maximum allowed carrying aircraft wheel capacity by FE modelling. Secondly, The Genetic Algorithm (GA) optimization technique is applied for maximizing the strength of the composite structure webs satisfying the minimum safety factor of five failure criteria of Tsai-Wu, Tsai-hill, Hoffman, Hashin and maximum stress. Finally, the achieved results promoted the usage of the composite structure to operate at the taxiways, runways, and theoretically, landing and take-off areas.

A data-driven equivalent circuit model’s parameter estimation method applied to Lithium-Titanate battery

Lithium battery systems are becoming essential for a variety of applications, such as maximisation of self-produced renewable energy in buildings, grid balances, and electric vehicles. Accurate and equivalent models are required to study and analyse the behaviour of batteries, both static and dynamic. This paper presents a novel data-driven parameter estimation procedure for high-current battery modelling, which has been applied to experimental measurement data conducted on Lithium Titanate batteries. The proposed estimation methodology has been applied to three different equivalent circuit models characterised by increasing modelling complexity. Genetic algorithm optimisation techniques solve the optimisation problem, estimating the model’s parameters and a look-up table of battery parameters estimated in discretised State of Charge. Enhance the battery operation modelling at high depth of discharge, the model is applied to the battery operated at high currents, covering its entire domain of state of charge. The fully depleted and charged state is given increased focus to maintain good modelling performance. Unlike previous works, the proposed methodology repeats the parameters estimation procedure for more than thirty-five state of charge ranges using non-equal width ranges to increase model density in critical regions. Based on the estimation results for one C-rate, it was found that the parameter estimation error ranges from 0.17% for the Rint model to 0.03% for the Dual-Polarisation model. The validation results confirmed that the proposed approach had a normalised root mean square error lower than 2%. The proposed methodology encompasses the entire process of parameter estimation, starting from raw experimental data. As such, it can be generalised and applied to other battery technologies.

OPTIMIZATION OF DISTRIBUTED GENERATION INSTALLATION IN CILEDUG TASIKMALAYA FEEDER TO REDUCE POWER LOSS WITH GENETIC ALGORITHM (GA) METHOD

Distributed generation (DG) is a small-scale power plant that is generally located close to the load. The installation of DG can be an alternative to reduce power losses in the distribution system. In this study, the placement and capacity optimization of DG to Ciledug feeder is carried out. Optimization is carried out using the genetic algorithm (GA) method which aims to reduce power losses in the system. The simulation is divided into 2 stages. In the first simulation before the installation of DG, the power loss obtained was 755,807 kW, and the voltage profile experienced under voltage from bus 31 to bus 104. The second simulation will be carried out by adding DG to the network, the DG used amounted to 2, 3, and 4 DG units. The location and capacity of DG are searched using the genetic algorithm optimization technique. The optimization results using 2 DGs, showed that the DGs were connected at bus 95 and bus 97 with a capacity of 0.969 MW and 0.480 MW, respectively. The addition of 2 DGs to the network reduces the power loss to 398,899 kW. Next, the optimization results using 3 DGs, showed that DGs were connected at bus 96, bus 78, and bus 67 with a capacity of 0.977 MW, 0.977 MW, and 0.013 MW, respectively. The addition of 3 DGs to the network reduces the power loss to 236,920 kW. Finally, the optimization results using 4 DGs show that DGs are connected at bus 67, bus 80, bus 46, and bus 26 with a capacity of 0.951 MW, 0.799 MW, 0.616 MW, and 0.257 MW, respectively. The addition of 4 DGs to the network reduces the power loss to 141,465 kW.

A Novel Method for the Analysis and Optimization of End Face Stress in Cycloidal Gears Based on Conformal Mapping

In the realm of precision engineering, the evaluation of stress distributions in complex geometries, such as the end face of gears, is paramount for determining performance characteristics, including the fatigue life of transmission devices. This research introduces a methodology that combines the method of conformal mapping for multiply connected regions with a proposed global scaling factor. This combination facilitates the transformation of stress computations from intricate geometries to a unit circle. By employing this method as a foundation for surrogate models, and by incorporating the Bayesian-enhanced least squares genetic algorithm optimization technique, this study refines the end face stress distribution, particularly considering variations induced by tooth profile modification errors. The results delineate a design parameter domain that is conducive to achieving uniform stress distribution, offering a refined perspective and methodology for the design of cycloidal wheels.

Optimum design and analysis of solar pump with the help of genetic algorithm, a MATLAB tool, and RSM tool at minimum cost

AbstractIn this research paper, a well‐performing solar pump at the minimum cost of project life is designed and installed. Initially, the capacity of submersible pumps and discharge have been determined based on the water requirement of the crop and the size of the field. The total number of solar panels, inverter size, and motor pump system have been designed based on the law of conservation of energy. The two optimization techniques genetic algorithm (GA) and response surface methodology (RSM) have been used for analyzing the pump performance parameters and total cost. The GA tool has been used to determine the optimum tilt angle on which responses namely solar flux, solar panel efficiency, exergy, and pump efficiency should be maximum. Moreover, the performance of the solar pump at the optimum tilt angle has been compared with the 28° fixed tilt angle (latitute of location). For the experimental study solar pump experimental setup of 5 hp power has been installed in Faculty of Engineering and Technology, MJP Rohilkhand University at 28.36°N, 79.43°E. The total cost of the solar pump has been optimized with the help of RSM based on the discharge and head of the solar pump. The new optimization approach reduced the levelized cost of electricity (LCOE) from 0.041 to 0.035 $/kWh and the payback period is reduced from 4 to 3 years. The annual increment in average solar flux and overall efficiency has been recorded to be from 606 to 697 W/m2 and 11% to 14% respectively.

Resilience and reliable integration of PV-wind and hydropower based 100% hybrid renewable energy system without any energy storage system for inaccessible area electrification

Achieving sustainable development goals requires off-grid electrification of remote areas using sustainable energy systems (SES). However, capacity planning for SES is challenging due to the long-term need to meet fluctuating demand and the intermittent and unpredictable nature of renewable energy (RE) sources. The best system size for resilience and reliable load servicing of a 100% hybrid renewable energy system has been investigated in this study. The ideal system configuration for a hybrid solar PV, wind, and hydro energy system has been achieved by applying the multi objective genetic algorithm (MOGA) optimization technique to assess optimal size of the renewable energy system. The PV/Wind/Hydro system has the lowest NPC and COE with the best target capabilities among all the communities examined, provided the system lasts 25 years, according to the NPC study. The most ideal configuration (PV 88 kW, wind 18 kW, Hydro1 215 kW, and Hydro2 197 kW) investigated for deployment of hybrid power system at the specified site has NPC, total system cost, and energy cost of $163,238 and $517,141, respectively, and $0.35/kWh. Finally, the implementation of this innovative proposal demonstrated that reliable load serving is possible with 100% renewable energy without the use of an energy storage device.

Parameter characterization of HT-PEMFC stack with a non-isothermal 3D model

This paper proposes a methodology for characterizing electrochemical parameters in non-isothermal three-dimensional (3D) simulation models of fuel cell stacks. The proposed methodology involves utilizing only easily measurable construction and non-invasive operational data. In order to achieve a reasonable computational cost, an iterative method developed on various 3D computational domains is combined with a genetic algorithm optimization technique. The effectiveness of the methodology is demonstrated through its application on a real scale 40-cell high temperature PEM fuel cell (HTPEMFC) stack, with the results indicating good agreement between the model and experimental data. This approach has the potential to significantly reduce the computational cost of optimizing fuel cell designs while still maintaining accuracy.

Optimal Positioning of Distributed Energy using Intelligent Hybrid Optimization

The renewable based energy source integration into the smart grid has seen a rapid surge lately and so has the role of distributed energy. By reducing the power losses within distribution network, the appropriate location and size of the distributed energy or distributed generation sources could be found. In this paper, it has been proposed that where the distribution generating units should be placed. An intelligent optimization technique has been utilized to position different distribution generators in the best possible way. These advancements have both the advantages and disadvantages of their own. The research employs a novel optimization technique based on a hybrid of genetic algorithm and particle swarm optimization techniques. Placing the distribution generations properly lowers the power losses and improves the profile of the voltage in the distribution system.