Genetic Algorithm Optimization Technique Research Articles

A Study of the Rainwater Pumping Station Operation Plan to Reduce River Water Level

Urban flood damage is caused by various and complex effects such as inland flood and external water flooding, so research on this is necessary. This study presented a rainwater pumping station operation technique that can suppress the rise in water levels in discharge rivers based on rainfall and pump operation scenarios in order to reduce inland water flooding and simultaneously consider external water levels. The research method used genetic algorithm (GA) optimization techniques to calculate the optimal operating level for each pump, and based on this, an operating scenario was created to minimize pump operation. In addition, after constructing a rainfall scenario based on the probability rainfall in Seoul, an optimal pump operation technique that can reduce the external water level while minimizing the inundation of domestic water was proposed. The results of the study showed that as the rainfall intensity increases, the flooding increases and the peak depth of the runoff increases. In addition, as the pumping scenario changes from 1 to 11, the number of pumping operations decreases, which reduces the drainage volume of the pumps, which increases the flooding.

Prediction model for hardness and tensile strength of graphene reinforced AZ 61 alloy based composite using Metaheuristic Algorithm

It is possible to produce improved material performance through the utilization of computational intelligence approaches such as genetic algorithms optimization technique which are discussed in this paper. The optimization of processes and the development of models that are driven by data are the key uses of these technologies. This article offers a comprehensive introduction of the topic of materials and discusses the ways in which computational intelligence techniques might be utilized to develop new materials. The present study envisages the development of data driven model that enables to derive desirable properties of the said composite; so, in order to secure the optimized subset of requirements (process parameters), a metaheuristic optimization tool is employed. We invoke the use of the Genetic Algorithm (GA) optimizing tool in association with linear regression, so as to achieve the best combination of hardness and tensile strength of AZ61 graphene nanoplate (GNP) composite.

Qualitative and quantitative reservoir characterization using seismic inversion based on particle swarm optimization and genetic algorithm: a comparative case study

Accurate reservoir characterization is necessary to effectively monitor, manage, and increase production. A seismic inversion methodology using a genetic algorithm (GA) and particle swarm optimization (PSO) technique is proposed in this study to characterize the reservoir both qualitatively and quantitatively. It is usually difficult and expensive to map deeper reservoirs in exploratory operations when using conventional approaches for reservoir characterization hence inversion based on advanced technique (GA and PSO) is proposed in this study. The main goal is to use GA and PSO to significantly lower the fitness (error) function between real seismic data and modeled synthetic data, which will allow us to estimate subsurface properties and accurately characterize the reservoir. Both techniques estimate subsurface properties in a comparable manner. Consequently, a qualitative and quantitative comparison is conducted between these two algorithms. Using two synthetic data and one real data from the Blackfoot field in Canada, the study examined subsurface acoustic impedance and porosity in the inter-well zone. Porosity and acoustic impedance are layer features, but seismic data is an interface property, hence these characteristics provide more useful and applicable reservoir information. The inverted results aid in the understanding of seismic data by providing incredibly high-resolution images of the subsurface. Both the GA and the PSO algorithms deliver outstanding results for both simulated and real data. The inverted section accurately delineated a high porosity zone (>20%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$>20\\%$$\\end{document}) that supported the high seismic amplitude anomaly by having a low acoustic impedance (6000–8500 m/s∗\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$*$$\\end{document} g/cc). This unusual zone is categorized as a reservoir (sand channel) and is located in the 1040–1065 ms time range. In this inversion process, after 400 iterations, the fitness error falls from 1 to 0.88 using GA optimization, compared to 1 to 0.25 using PSO. The convergence time for GA is 670,680 s, but the convergence time for PSO optimization is 356,400 s, showing that the former requires 88%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$88\\%$$\\end{document} more time than the latter.

Modeling of genetic algorithm tuned adaptive fuzzy fractional order PID speed control of permanent magnet synchronous motor for electric vehicle

This study presents a novel Genetic Algorithm-optimized Adaptive Fuzzy Fractional Order Proportional Integral Derivative (GA-AFFFOPID) controller for enhancing the speed control performance of permanent magnet synchronous motor (PMSM) drives in Electric Vehicles. The proposed GA-AFFFOPID controller, which combines the advantages of genetic algorithm optimization and adaptive fuzzy fractional-order PID control, represents a unique and innovative approach to address the control challenges associated with PMSM drives. Permanent magnet synchronous motor technology, known for its efficiency, compactness, reliability, and versatility in motion control applications, is increasingly adopted in electric vehicle drive systems. However, the inherent non-linearity, dynamics, and uncertainties of permanent magnet synchronous motors pose significant control challenges. The exceptional performance of the GA-AFFFOPID controller, demonstrated through its superior system dynamics, precise speed tracking, and robustness against parameter variations and sudden load disturbances, underscores the significant advancements enabled by the genetic algorithm optimization technique in improving the control performance of PMSM drives for electric vehicle applications. Comparative analysis with traditional control methods demonstrates the exceptional performance of the Genetic Algorithm-optimized Adaptive Fuzzy Fractional Order Proportional Integral Derivative controller. These findings highlight the significant performance improvements facilitated by the genetic algorithm optimization technique in enhancing the control performance of the adaptive fuzzy fractional order PID controller in PMSM drives for electric vehicle applications.

Thermal performance analysis and optimization of a heat pipe-based electronic thermal management system using experimental data-driven neuro-genetic technique

Heat pipes are highly efficient heat transfer devices and are used widely to dissipate heat generated by electronic components, thereby maintaining their operating temperatures and preventing overheating. The present study deals with the thermal performance prediction of a cylindrical heat pipe (length 380 mm) based electronic thermal management system using steady-state experimental investigation. The experiments are conducted under varying operational conditions, including heat inputs (10–50 W), condenser cooling water inlet temperatures (288.15, 293.15, and 298.15 K), and flow rates (10, 25, and 40 LPH). The study shows that the evaporator temperature of the heat pipe consistently rises with the increase in heat inputs for all tested conditions. The lowest evaporator temperature (311.42 K) is noticed for the cooling water inlet temperature of 288.15 K across all heat inputs. Notably, at 10 LPH, the heat pipe supplied with cooling water at 288.15 K exhibits reductions of up to 1.47 % and 2.29 % compared to 293.15 K and 298.15 K, respectively. Similar trends are also observed at 25 LPH and 40 LPH. The heat pipe’s thermal resistance is lowest (0.95 K/W) at a cooling water inlet temperature of 298.15 K. At 10 LPH, there are reductions of 10.19 % and 5.62 % compared to 288.15 K and 293.15 K, and at 40 LPH, reductions are 15.66 % and 7.89 %. The heat pipe’s evaporator heat transfer coefficient also plays a significant role and is maximum for the cooling water inlet temperature of 288.15 K at a flow rate of 10 LPH. To understand the complex behavior of the heat pipe and for better prediction of its thermal performance, a neuro-genetic (experimental data-driven artificial neural network and genetic algorithm) optimization technique is considered in the study. This predicts the optimal combination of operating parameters (heat input, cooling water inlet temperature, and flow rate) to minimize the heat pipe’s evaporator wall temperature which is found to be 312.54 K at a heat input of 10 W, flow rate of 10 LPH, and cooling water inlet temperature of 289.14 K. These input combinations are further validated through the experiments and the error in the heat pipe’s evaporator temperature is found to be 0.66 % only. Overall, this neuro-genetic technique underscores the importance of optimizing operational parameters for enhanced heat pipe performance and offers valuable insights for electronic cooling systems.

An optimal B-spline approach for vectorizing raster image outlines.

Capturing outlines is crucial in vectorization of the digital images. An effective and automatic algorithm is presented for outline vectorizing of planar objects within the digital images using trigonometric B-spline. A soft computing optimization technique Genetic Algorithm (GA) is employed to determine the suitable measures of parameter in the description of proposed B-spline. The anticipated scheme is executed on a few raster (bitmap), digital images to validate the robustness of the algorithm. The procedure of vectorizing outlines encompasses a series of stages like boundary detection, corner recognition, break point identification and curve fitting using the proposed B-spline.

A two-stage optimization model for relief distribution to disaster survivors under two-fold uncertainty

Disasters are unforeseen occurrences requiring extensive transport deployment to support and relieve victims. Sometimes, this transportation is not feasible directly from some supply points to some destination points. Due to this tragedy, it is unclear precisely what is available at supply points, what is needed at destinations, how much transportation capacity there is, and what the routes are like. In this study, we investigate a two-stage multi-item fixed charge four-dimensional transportation problem using the concept of big data theory under the two-fold uncertainties. Here, the model’s parameters such as unit transportation costs, availabilities of items at the suppliers, fixed charges, capacities of conveyances, and demands of the items at the retailers are considered type-2 zigzag uncertain variables. Using big data theory and based on uncertain programming theory, two novel uncertain models are developed such as chance-constrained programming and expected value programming model. These two uncertain models transformed into the deterministic form via uncertainty inverse distribution theory. A critical value based reduction method with three categories (i.e., expected value, pessimistic value, and optimistic value) is applied to reduce the type-2 zigzag uncertain variable to the type-1 zigzag uncertain variable. The genetic algorithm and particle swarm optimization techniques have been proposed to find the optimal solution for the two deterministic models. The efficiency of our proposed approach is demonstrated with a real-life numerical example.

Design of electrostatic lenses through genetic algorithm and particle swarm optimisation methods integrated with differential algebra

Genetic algorithm (GA) and particle swarm optimisation (PSO) techniques have been integrated with the differential algebra (DA) method in charged particle optics to optimise an Einzel lens. The DA method is a robust and efficient tool for the calculation of aberration coefficients of electrostatic lenses, which makes use of nonstandard analysis for ray tracing a particle as it is subjected to the field generated by a lens. In this study, initial populations of lenses with random geometrical configurations are generated. These initial populations are then subjected to GA and PSO algorithms to alter the geometry of each lens for a set number of iterations. The lens performance is evaluated by calculating the spot size using the aberrations coefficients up to third-order generated by the DA method. Moreover, a focusing column comprising two lenses and a Wien filter was optimised using GA method.

An Innovative Approach for Mission Sharing and Route Planning of Swarm Unmanned Aerial Vehicles in Disaster Management

Fast and effective response in disaster situations is critical for the success of rescue operations. In this context, swarm Unmanned Aerial Vehicles (UAVs) play an important role in disaster response by rapidly scanning large areas and performing situation assessments. In this paper, we propose an innovative method for task allocation and route planning for swarm UAVs. By combining Genetic Algorithm (GA) and Ant Colony Optimization (ACO) techniques, this method aims to ensure the most efficient movement of UAVs. First, clusters are created using GA to determine the regions of the disaster area that need to be scanned. At this stage, factors such as the capacities of the UAVs, their flight times, and the breadth of their mission areas are taken into account. Each UAV is optimized to scan a specific area assigned to it. Once the clusters are formed, the routes of the UAVs within each cluster are determined by the Ant Colony Algorithm (ACA). The route planning is tested both on Google Maps and in a simulation environment. Google Maps is used to evaluate the accuracy and feasibility of route planning based on real-world conditions, while the simulation environment provides the opportunity to test the behavior of the UAVs and the effectiveness of the routes in a virtual setting. With real-time data integration, the UAVs' route planning can be updated instantly and quickly adapted to emergency situations.

Experimental Investigations and Optimization of Surface Roughness Using Response Surface Methodology Coupled with Genetic Algorithm and Particle Swarm Optimization Techniques in Grinding of Inconel 718

Experimental Investigations and Optimization of Surface Roughness Using Response Surface Methodology Coupled with Genetic Algorithm and Particle Swarm Optimization Techniques in Grinding of Inconel 718

Establishing a unified viscoplastic constitutive equation for EA4T steel: Comparative analysis with Arrhenius model

Quasi-static and impact compression experiments were conducted on EA4T steel using a Gleeble-3800 thermal simulation machine. The study aimed to investigate the complex microstructural evolution and thermal deformation behavior of EA4T steel under various experimental conditions, including temperatures ranging from 970 °C to 1170 °C and strain rates ranging from 0.01s−1 to 1s−1. To precisely elucidate these phenomena, we meticulously constructed a unified visco-plastic constitutive model using the internal variable methodology. The model's parameterization was achieved through the effective application of genetic algorithm optimization techniques. Rigorous validation of the model was performed by meticulously comparing its outputs with experimental data, including key metrics such as average grain size, recrystallized fraction, and effective flow stress. In addition,a comparative analysis with the improved Arrhenius model highlights the superior performance of the unified visco-plastic constitutive equation in capturing the intricate microstructural evolution and thermal deformation behavior exhibited by EA4T steel.

Optimizing capacitor size and placement in radial distribution networks for maximum efficiency

As distribution systems continue to expand, they face challenges such as increased system losses and inadequate voltage regulation. To address these issues, shunt capacitors are being deployed in distribution networks. These capacitors offer reactive power compensation, enhance power factor, improve voltage profiles, promote system stability, and significantly reduce losses. However, determining the appropriate capacitor sizes and their optimal placements requires careful consideration of both technical and economic factors. The nonlinear nature of optimal capacitor placement and sizing, leveraging optimization techniques becomes crucial in identifying the best locations and values for capacitors. This paper demonstrates the effective utilization of Particle Swarm Optimization (PSO) and Real Coded Genetic Algorithm (RCGA) optimization techniques for capacitor placement and selection. The optimization techniques are applied to a 33-bus IEEE standard radial distribution system, to reduce the real power loss and to improve the voltage profile considering both constant and variable loads. Both PSO and RCGA algorithms identify suitable locations for the placement of capacitors for reactive power compensation within the distribution system. By optimizing the objective function associated with capacitor placement costs and maximizing annual cost savings, the PSO and RCGA techniques yield promising results. After implementing the optimal capacitor placements at the identified candidate nodes, a significant reduction in losses within the radial distribution system is observed. Moreover, the cost savings achieved through optimal placement and sizing are substantial.

AI-based optimal allocation of BESS, EV charging station and DG in distribution network for losses reduction and peak load shaving

In response to the growing integration of battery energy storage systems (BESS), electric vehicles (EV), and distributed generation (DG), planning frameworks have emerged to optimize the deployment of these units within distribution networks. In this context, this study introduces an artificial intelligence (AI) approach utilizing the genetic algorithm optimization technique to identify optimal installation nodes for BESS, EV charging stations, and DG units within the grid. The framework aims to minimize energy losses in the distribution system, incorporating a peak load shaving strategy in the BESS. The mathematical model encompasses technical and operational constraints from the resources and the grid. To address the complexity ensuing from exploring an extensive solution space, handling constraints, and managing the decision variables, a real coded genetic algorithm is employed to determine the optimal locations. Through simulations on a distribution feeder, the study demonstrates that resources installed based on the proposed approach significantly benefit the grid. This is evidenced by a reduction in energy losses, improved voltage levels, and demand mitigation during peak periods. An analysis of the BESS impact on losses, with respect to its installation nodes, is also conducted. Furthermore, the optimization approach is validated using benchmark functions and compared with another method.

Structural optimization using a genetic algorithm aiming for the minimum mass of vertical axis wind turbines using composite materials

A wind turbine comprises multiple components constructed from diverse materials. This complexity introduces challenges in designing the blade structure. In this study, we developed a structural optimization framework for Vertical Axis Wind Turbines (VAWT). This framework integrates a parametric Finite Element Analysis (FEA) model, which simulates the structure's global behavior, with a Genetic Algorithm (GA) optimization technique that navigates the design domain to identify optimal parameters. The goal is to minimize the mass of VAWT structures while adhering to a suite of complex constraints. This framework quantifies the mass reduction impact attributable to material selection and structural designs. The optimization cases indicate that blades made from Carbon Fiber Reinforced Plastics (CFRP) materials are 47.1 % lighter than those made from Glass Fiber Reinforced Plastics (GFRP), while the structural parts are 44.8 % lighter. This work also provides further recommendations regarding the scale and design of the structures. With the materials and structural design established, future studies can expand to include more load cases and detailed designs of specific components.

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.

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.

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.