Genetic Algorithm Based Optimization Technique Research Articles

Techno-economic optimization and working fluid selection of a biogas-based dual-loop bi-evaporator ejector cooling cycle involving power-to-hydrogen and water facilities

Biogas-fueled decentralized energy systems featuring gas turbines emerge as pivotal players in the quest for more adaptable and eco-friendly energy solutions. This study presents the design of a cutting-edge multigeneration system that harnesses the potential of a gas turbine coupled with ejector-driven dual-loop bi-evaporator technology, reverse osmosis desalination, proton exchange membrane electrolysis, and an organic Rankine cycle. The research encompasses an extensive sensitivity analysis and deploys a genetic algorithm-based optimization technique. Through meticulous Optimization, the system achieves remarkable outcomes, including electricity generation, cooling capacity, heating capacity, desalinated water production, and hydrogen yield, measured at 957.3 kW, 231.4 kW, 272.3 kW, 7.336 kg/s, and 0.99 kg/h, respectively. Notably, this performance surpasses the base case by 2% and 8.9%, yielding exergy efficiency and total unit cost improvements of 33.3% and 16.4 $/GJ. Among the critical decisions made was selecting an organic working fluid for the organic Rankine cycle. Through rigorous evaluation, isobutene emerged as the optimal choice, demonstrating significantly improved output power compared to alternatives. Isobutane as the working fluid led to a substantial increase in overall exergy efficiency and a resultant total unit cost of 18.06 $/GJ, accompanied by an impressive 23.48% exergy efficiency.

Analysis of the Theory and Traffic Scheduling for Transit Network by Genetic Algorithm-Based Optimization Technique

Analysis of the Theory and Traffic Scheduling for Transit Network by Genetic Algorithm-Based Optimization Technique

Analysis of multimodal performance of a hybrid solar pumped storage system for enhanced energy security in rural areas

ABSTRACT Remote hilly hamlets in India still face acute energy security problems. As a solution, the present study discusses a Novel Hybrid Solar Pumped Storage Powerplant that can operate in grid-connected and standalone modes to provide stable power throughout the year. Genetic algorithm-based optimization techniques and actual resource and demand data were utilized to optimize the system’s design. Site-specific power generation and grid interaction models were established and multiple operational modes of the system were simulated with a system dynamics modeling approach. Results show a 73% reduction in greenhouse emissions, a levelized energy cost of 0.09$/kWh, and assured generation security of 82% during standalone operation. The probability of power supply loss during the grid-connected operation was minimized, and 20% was incurred during standalone operation. This innovative solution can provide reliable and sustainable power to remote hilly hamlets, contributing to the Sustainable Development Goal (SDG)-7 and helping India achieve its COP-26 & 27 commitments. As the government takes active initiatives to improve grid stability in remote locations, this system can be effectively implemented and scaled up to provide a cost-effective and environmentally friendly solution for storage and utilization of solar power.

A Novel PI-PD Controller Tuning Method based on Neutrosophic Similarity Measure for Unstable and Integrating Processes with Time Delay

Integrating systems and unstable systems are two types of systems that are widely used in various industries, including control engineering and electrical engineering. However, controlling these systems can be a daunting task due to their inherent complexity. To address this challenge, the PI-PD controller structure has been widely adopted in the industry, as it has proven to be very successful in controlling integrating and unstable systems. In this paper, a new design method is proposed to determine the optimal controller parameters in the control of integrating and unstable systems with time delay. The design method utilizes a Genetic Algorithm-based optimization technique, which incorporates a new objective function that is based on the neutrosophic similarity measure. Two different types of plants have been chosen as examples in this study. The first plant is a time-delayed integrating system. The second plant is an unstable system, which means that the output signal is very sensitive to any changes or disturbances in the input signal. This study examines the parameter uncertainties of systems by comparing them with some design methods from the literature. The results are presented comparatively in figures and tables to show the superiority of the proposed method.

Frequency Control in an Autonomous Microgrid Using GA Based Optimization Technique

In recent times the rapid development of distributed energy sources has transformed the conventional electrical grid to a decentralised system. This has led to the advancement in research of microgrid. In the conventional grid, the voltage and frequency regulation depends on the speed control of alternators connected to the grid. But for an autonomous microgrid, the voltage and frequency has to be regulated independent of the main grid. Deviation in the frequency occurs whenever there is change in the load and due to inherent variability of distributed energy sources. This deviation can be regulated by optimising the droop coefficients using Genetic algorithm (GA). Simulations have been carried out in MATLAB/SIMULINK for different types of loads (linear and non-linear) and results are shown for frequency deviation, and active power sharing of the DGs. The responses for frequency deviations with and without GA optimizations are presented.

Optimization of processing parameters of cold metal transfer joined 316L and weld bead profile influenced by temperature distribution based on genetic algorithm

Austenitic stainless steel alloys find the wide range of application in modern industries like pipework, containers, food production and in medical industries for its excellent processing properties and corrosion resistance. There is enormous literature report on the mechanical properties, appropriate joining of materials using different fusion welding processes. Consequently, the cold metal transfer technique appears to weld materials with low heat input which is a noticeable feature of this welding process. In this paper, cold metal transfer welding is performed on austenitic stainless steel material 316L and its bead geometries such as reinforcement height, depth of weld penetration and bead width profile are examined. The temperature distribution at the welding line is observed by means of the data acquisition unit. Genetic algorithm based optimization technique is used to achieve the desired combination of input variables and weld bead geometry. This developed genetic algorithm optimizes the welding process parameters and geometry of the weld bead, by minimizing the least square error based objective function. The investigation outcome of this paper provides an insight into the characterization of the weldment, the effects of weld current and weld travel speed on temperature profile and mechanical properties include hardness, tensile and residual profiles.

A new federated genetic algorithm-based optimization technique for multi-criteria vehicle route planning using ArcGIS network analyst

PurposeUsing a real-time road network combined with historical traffic data for Al-Salt city, the paper aims to propose a new federated genetic algorithm (GA)-based optimization technique to solve the dynamic vehicle routing problem. Using a GA solver, the estimated routing time for 300 chromosomes (routes) was the shortest and most efficient over 30 generations.Design/methodology/approachIn transportation systems, the objective of route planning techniques has been revised from focusing on road directors to road users. As a result, the new transportation systems use advanced technologies to support drivers and provide them with the road information they need and the services they require to reduce traffic congestion and improve routing problems. In recent decades, numerous studies have been conducted on how to find an efficient and suitable route for vehicles, known as the vehicle routing problem (VRP). To identify the best route, VRP uses real-time information-acquired geographical information systems (GIS) tools.FindingsThis study aims to develop a route planning tool using ArcGIS network analyst to enhance both cost and service quality measures, taking into account several factors to determine the best route based on the users’ preferences.Originality/valueFurthermore, developing a route planning tool using ArcGIS network analyst to enhance both cost and service quality measures, taking into account several factors to determine the best route based on the users’ preferences. An adaptive genetic algorithm (GA) is used to determine the optimal time route, taking into account factors that affect vehicle arrival times and cause delays. In addition, ArcGIS' Network Analyst tool is used to determine the best route based on the user's preferences using a real-time map.

Optimizing Daylight Glare and Circadian Entrainment in a Daylight-Artificial Light Integrated Scheme

Human circadian rhythm varies with different combinations of light sources. Natural light and dark patterns regulate the sleeping and waking cycles of the human body. This study aimed to develop a simplified model for daylight glare and circadian entrainment in an existing controlled daylight-artificial light integrated system. In addition, the effects of window orientation, occupant position, artificial light, daylight, and a combination of both on Circadian Stimulus (CS) were investigated. In a daylit interior space, window orientation, shading control and occupant seating position play a significant role in the CS. Simplified models to predict Daylight Glare Probability (DGP) and CS were estimated from the dataset. The developed nonlinear CS estimation models have R-squared values of 0.983 and 0.974. The precision of the models was evaluated in terms of standard error of regression (S) and AIC score. Multi-Objective Genetic Algorithm based optimization technique was developed using the above models to minimize glare and maximize CS in the working space. This optimisation methodology aids in implementing a complete automation algorithm for the test workbench to achieve visual comfort and circadian entrainment.

Optimization of Power Density of Axial Flux Permanent Magnet Brushless DC Motor for Electric Two-Wheeler

The main objective of this work is to optimize the power density of axial flux permanent magnet brushless dc (PMBLDC) motor based on genetic algorithm (GA) technique for performance improvement of electric 2-wheeler. Power density is one of the important performance parameter of motor as it significantly influences overall performance of electric 2-wheeler. Firstly, the rating of electric motor is determined according to the application requirements and vehicular dynamics. Axial flux PMBLDC motor of 250 W, 150 rpm is designed to fit in to the rim of electric 2-wheeler based on assumption of various design variables. The salient contribution of this work is to suggest the best combination of design variables with the application of GA optimization technique for power density optimization. Comparative performance analysis is carried out between initially designed motor and optimized motor. Finally, 3 dimensional (3-D) finite element analysis (FEA) is performed to verify the results obtained from design optimization. Results obtained from FEA fairly validates the initial design and optimized design. It is analyzed that the power density of motor is enhanced by 42.85 % with the proposed optimization technique. The proposed technique is implementable and complexity free. It may further be applied to the performance improvement of a non-linear design comprising different design variables.
 HIGHLIGHTS
 
 Axial flux permanent magnet motors are the most compatible in electric vehicle applications
 Power density is one of the important performance parameters of axial flux permanent magnet motors
 Optimization of power density improves drive range and overall performance of electric vehicle
 Influential design variables are identified based on parametric analysis and its optimization is carried out with an GA based optimization technique with an objective of power density optimization
 Proposed optimization technique is validated with finite element analysis
 
 GRAPHICAL ABSTRACT

Optimal receive beamforming in spatial antenna diversity system using evolutionary genetic algorithm

Frequent packet loss and power inadequacy are the major issues in wireless fading networks owing to the deep fading effects and inefficient resource utilization caused by unoriented information forwarding mechanisms. Diversity techniques are potentially employed in wireless communication systems to combat the impact of fading and reinforce the desired service level guarantees for data transmission. In this work, we employ spatial diversity and antenna beamforming methods to significantly improve the signal quality performance of wireless networks and enhance the reliability by decreasing the bit error probability. A constrained optimization problem is formulated for alleviating the net noise power integrated with the adaptive beamforming vector and ameliorated channel state information. For this, the evolutionary genetic algorithm scheme is iteratively implemented for evaluating the optimal solution of the proposed optimization problem. The influence of various selection functions in the application of genetic algorithm is investigated for effective heuristic modeling design and searching through the feasible solution space. Through extensive simulation results, several crucial network parameters characterizing the proficiency of wireless communications including the noise power, signal-to-noise ratio (SNR), bit error rate (BER) are estimated and analyzed for varying number of antennas deployed at the receiver node. Furthermore, the performance of the proposed genetic algorithm based optimization technique is compared with the existing models using the key channel quality metrics of achievable SNR, BER, and model computational complexity. • Spatial diversity and receive beamforming techniques can be jointly implemented. • This optimization model achieves enhanced signal quality and reliability performance. • Evolutionary genetic algorithm solves the problem using intelligent heuristic design. • Disparate selection functions exhibit different iterative complexity for optimization. • Simulation is used to show the improved noise power, SNR, BER with evolving antennas.

A data-driven optimization model for coagulant dosage decision in industrial wastewater treatment

Industrial wastewater treatment is one of the critical issues faced by water utilities. With the dynamic and complex characteristics, to properly utilize the wastewater treatment and optimize its use is particularly a challenge. A coagulation-flocculation process is a widely used method in wastewater treatment where the complexity of the coagulant chemical theory shows the difficulties to determine the coagulant dosage efficiently. In the current study, a data-driven approach that combines a genetic algorithm-based optimization and particle swarm optimization technique with the regression model analyses was implemented to optimize the coagulant dosage. By so doing, the effluent quality and the sludge level generated from the printed circuit board manufacturing wastewater plant were improved. To evaluate the performance of the model, statistical analysis to determine the significance of the parameters and the determination coefficient R2 has been used, and the Cu removal regression model showed good agreement (R2= 99.97%). During the optimizing process, the model improved 27% of the emission standard target for outgoing Cu while assuring the quality of sludge level at the same level. The results also demonstrated that the proposed data-driven approach lowers cost by 10% in the examined wastewater treatment plant. Through the proper treatment and the control of the chemical dosage for Cu removal based on practical data, the proposed model can be helpful to the engineering applications.

A Novel Active Optimization Approach for Rapid and Efficient Design Space Exploration Using Ensemble Machine Learning

Abstract In this work, a novel design optimization technique based on active learning, which involves dynamic exploration and exploitation of the design space of interest using an ensemble of machine learning algorithms, is presented. In this approach, a hybrid methodology incorporating an explorative weak learner (regularized basis function model) that fits high-level information about the response surface and an exploitative strong learner (based on committee machine) that fits finer details around promising regions identified by the weak learner is employed. For each design iteration, an aristocratic approach is used to select a set of nominees, where points that meet a threshold merit value as predicted by the weak learner are selected for evaluation. In addition to these points, the global optimum as predicted by the strong learner is also evaluated to enable rapid convergence to the actual global optimum once the most promising region has been identified by the optimizer. This methodology is first tested by applying it to the optimization of a two-dimensional multi-modal surface and, subsequently, to a complex internal combustion (IC) engine combustion optimization case with nine control parameters related to fuel injection, initial thermodynamic conditions, and in-cylinder flow. It is found that the new approach significantly lowers the number of function evaluations that are needed to reach the optimum design configuration (by up to 80%) when compared to conventional optimization techniques, such as particle swarm and genetic algorithm-based optimization techniques.

Optimal Control Strategies for Switchable Roof Insulation Systems Applied to US Residential Buildings

AbstractSwitchable building envelope systems, including passive and active systems, have recently seen an increase interest in the literature. Unlike static insulation, switchable insulation systems (SISs) have the ability to adjust the thermal properties of envelope elements. Advanced control strategies for SISs are evaluated in this analysis using genetic algorithm-based optimization techniques. In particular, this study investigates the potential heating and cooling energy savings for deploying optimal controls specific to SIS technologies when applied to residential roofs located in representative US climates. Moreover, energy use and peak demand savings obtained by optimal controls are compared with those obtained from the 2-step rule-based controls. Overall, the analysis results indicate that the maximum monthly additional savings obtained by optimal controls can reach up to 32% compared with 2-step rule sets when an annual analysis is conducted for a residential building located in Golden, CO.

Optimization of Specific Power of Surface Mounted Axial Flux Permanent Magnet Brushless DC Motor for Electrical Vehicle Application

Optimization of specific power of axial flux permanent magnet brushless DC (PMBLDC) motor based on genetic algorithm optimization technique for an electric vehicle application is presented. Double rotor sandwiched stator topology of axial flux permanent magnet brushless DC motor is selected considering its best suitability in electric vehicle applications. Rating of electric motor is determined based on vehicular dynamics and application needs. Double rotor sandwiched stator axial flux PMBLDC motor is designed considering various assumed design variables. Initially designed axial flux PMBLDC motor is considered as a reference motor for further analysis. Optimization of the specific power of electric motor for electric vehicle applications is a very important design issue. The Genetic Algorithm (GA) based optimization technique is proposed for optimization of specific power of axial flux permanent magnet brushless DC motor. Optimization with an objective of maximum specific power with the same torque rating is performed. Three-dimensional finite element analysis is performed to validate the proposed GA based specific power optimization. Close agreement between results obtained from finite element analysis and analytical design establishes the correctness of the proposed optimization technique. The performance of the improved motor is compared with the initially designed reference motor. It is analyzed that the specific power of axial flux PMBLDC motor is enhanced effectively with the application of GA based design optimization technique.

On accurate localization of sensor nodes in underwater sensor networks: a Doppler shift and modified genetic algorithm based localization technique

The problem of localization in under water sensor nodes has led to proposal of many techniques over the past few decades that depend primarily on Time of Arrival and Time Difference of Arrival. While these techniques are intuitively very appealing and easy to deploy, accurate node localization in dynamic under water environment has remained elusive. Sensor nodes deployed underwater tend to move from their original positions due to water currents and hence their exact positions at a given moment of time are not known with precision. Due to inherent drawbacks of radio signal propagation in underwater environment, localization of sensor nodes depends on acoustic signals. In this paper, we propose a Doppler shift based localization followed by a genetic algorithm based optimization technique that improves accuracy in localizing unknown nodes in underwater sensor networks. The proposed technique envisages sink nodes playing a pivotal role in taking over a bulk of the computational load on account of being comparatively more accessible and serviceable as compared to any other nodes in the network that are deployed underwater. The algorithm relies on observed frequency shifts (Doppler shift) of sound waves compared to actual, that happen when source and observer are mobile as they do in a marine environment. While Doppler shift determines the approximate location of an unknown sensor node, genetic algorithm minimizes the error in localization. Our proposed methodology has much lower localization error as compared to existing protocols.

Identification of Microfluidization Processing Conditions for Quality Retention of Sugarcane Juice Using Genetic Algorithm

Microfluidization is a novel non-thermal technology which encompasses high-velocity impact, intense shear, and occasionally cavitation on liquid-liquid or solid-liquid systems. In this work, sugarcane (Saccharum officinarum L.) juice was microfluidized at three different pressures (100, 120, 140 MPa) with two different passes (1 and 2). The effect of microfluidization on the total soluble solids (TSS), total phenolic content (TPC), free radical scavenging activity (FRSA), total flavonoid content (TFC), chlorophyll, color, polyphenol oxidase (PPO) activity and microbiological quality was evaluated. Both TSS and PPO showed a gradual decrease (p < 0.05) with the increase in pressure and passes indicating particle breakdown and enzyme inactivation, respectively. At 120 MPa, the TPC, TFC, and FRSA of the juice increased whereas a reduction in values was observed at pressure levels of 100 MPa and 140 MPa. No visual discoloration was observed in the microfluidized sample as compared with the control sample. The chlorophyll content of the treated juice increased significantly with pressure (p < 0.05). Sensory attributes showed no difference between fresh and processed sample. Treatment condition of 124 MPa/2 passes was found most effective in maintaining the quality of sugarcane juice based on a genetic algorithm–based optimization technique. Microbiological and sensory analysis suggested a shelf life of 6 days for microfluidized sugarcane juice over a < 2-day shelf life for control at a storage temperature of 4 °C.

An effective image retrieval based on optimized genetic algorithm utilized a novel SVM-based convolutional neural network classifier

Image retrieval is the process of retrieving images from a database. Certain algorithms have been used for traditional image retrieval. However, such retrieval involves certain limitations, such as manual image annotation, ineffective feature extraction, inability capability to handle complex queries, increased time required, and production of less accurate results. To overcome these issues, an effective image retrieval method is proposed in this study. This work intends to effectively retrieve images using a best feature extraction process. In the preprocessing of this study, a Gaussian filtering technique is used to remove the unwanted data present in the dataset. After preprocessing, feature extraction is applied to extract features, such as texture and color. Here, the texture feature is categorized as a gray level cooccurrence matrix, whereas the novel statistical and color features are considered image intensity-based color features. These features are clustered by k-means clustering for label formation. A modified genetic algorithm is used to optimize the features, and these features are classified using a novel SVM-based convolutional neural network (NSVMBCNN). Then, the performance is evaluated in terms of sensitivity, specificity, precision, recall, retrieval and recognition rate. The proposed feature extraction and modified genetic algorithm-based optimization technique outperforms existing techniques in experiments, with four different datasets used to test the proposed model. The performance of the proposed method is also better than those of the existing (RVM) regression vector machine, DSCOP, as well as the local directional order pattern (LDOP) and color co-occurrence feature + bit pattern feature (CCF + BPF) methods, in terms of the precision, recall, accuracy, sensitivity and specificity of the NSVMBCNN.

Sensitivity analysis on globularized fraction of α lamellae in titanium alloys

Abstract The sensitivity analysis functions on globularized fraction of α lamellae were established using a physically-based microstructure model and gradient method. These functions were applied to the sensitivity analysis on globularized fraction of α lamellae in TC17 alloy. The material constants in these functions are determined using the genetic algorithm-based objective optimization technique. The globularized fraction of α lamellae during isothermal compression of TC17 alloy was quantitatively analyzed based on scanning electron microscopy (SEM) observation. The results show that α lamellae mostly change to equiaxed α grains at a deformation temperature of 1083 K, a strain rate of 0.01 s−1 and a strain of 1.2. The globularized fraction decreases with increasing strain rate because lower strain rate provides enough time for the spheroidization. The effect of deformation temperature on the globularized fraction is controlled by the strain rate. And, the predicted derivations of globularized fraction with respect to processing parameters show good agreement with the experimental values.

Multi-objective Optimization of EDM Process with Performance Appraisal of GA based Algorithms in Neural Network Environment

Abstract This work focuses on the material removal rate and electrode wear rate of A2 steel in the EDM process, which are considered as to be the important governing parameters for higher productivity and accuracy. The purpose of this investigation is to minimize the electrode wear rate and maximize the material removal rate by controlling the machining parameters. In this work, an experimental investigation has been carried out by considering four machining control parameters such as discharge current (Ip), pulse duration (Ton), duty cycle (τ or Tau) and voltage (V) by using the full factorial design methodology. Artificial Neural Network (ANN) model has been developed to correlate the data generated from experimental results. To obtain an efficient ANN model and to achieve minimum prediction error, ANN architectures, learning/training algorithms and numbers of hidden neurons are generally varied. However, so far the variations have been made in a random manner. For this reason a full factorial design integrated Analysis of Variance (ANOVA) has been utilized to investigate the influence of control factors on response. Developed ANN model equation of material removal rate and electrode wear rate were subsequently used as the fitness functions in Genetic Algorithm (GA) based multi-objective algorithms. Three advanced GA based optimization techniques, i.e., Non-dominating Sorting Genetic Algorithm-II (NSGA-II), controlled NSGA-II and Strength Pareto Evolutionary Algorithm 2 (SPEA2) were attempted for the considered EDM process. Furthermore, a non-dominated set of solution was obtained to have diversity in the solutions for the EDM process. The result obtained using the ANN method and optimization techniques were confirmed using confirmation experiments.

Evaluation of ratcheting behaviour in cyclically stable steels through use of a combined kinematic-isotropic hardening rule and a genetic algorithm optimization technique

Modeling of material behaviour under strain-controlled and stress-controlled loading for cyclically stable materials (CSMs) is generally done using kinematic hardening, neglecting the isotropic hardening component. Although this approach is able to simulate the hysteresis loops, the quantitative ratcheting characteristics are often not of the desired accuracy; thus earlier investigators have introduced different modifications in the kinematic hardening component for case to case basis to obtain better agreement to the experimentally observed cyclic-plastic behaviour. Combined hardening models, though used for cyclic softening/hardening materials, have not been used for cyclically stable materials to the best knowledge of the authors. The primary aim of this study is to improve the ratcheting predictions for CSMs considering the well-known Chaboche's combined hardening model using a new generalized methodology. The initial estimates of the hardening components are obtained from symmetric strain-controlled tests subjected to their refinement by genetic algorithm based optimization technique. The Chaboche's parameters have been evaluated using the proposed methodology for available reported experimental data on cyclically stable steels to demonstrate the response of materials under stress-controlled and strain-controlled loading conditions.