Genetic Algorithm Methodology Research Articles

Improving prediction accuracy of open shop scheduling problems using hybrid artificial neural network and genetic algorithm

Scheduling issues are typically classified as constrained optimization problems that examine the allocation of machines and the sequence in which tasks are processed. Regarding the existence of one machine, identification of works processing sequence forms a complete time schedule. Therefore, following a review of previous works, the goal of the present study is designing a mathematical model for open shop scheduling (OSS) problems using different machines aiming at minimizing the maximum time required to complete the works using an artificial neural network (ANN) and genetic algorithm (GA). The research data were driven from a Shoe company carried out between the years 2019 and 2020. The GA and ANN methodologies were employed to analyze and forecast the scheduling of activities within the shoe manufacturing sector. The findings indicated that the probability associated with the third population of the GA was 0.15. Furthermore, an examination of the average values of standard error revealed that the neural network model outperformed in terms of predictive accuracy. The estimated minimum time necessary for task completion, as determined by the neural network, was calculated to be 0.96699, facilitating an optimal condition for meeting the established objectives.

Investigations on solar PV integration and associated power quality challenges in distribution systems through the application of MCS and GA

In the forthcoming decades, significant advancements will shape the construction and operations of distribution systems. Particularly, the increasing prominence of photovoltaic (PV) systems in the power industry will impact the security of these systems. This study employs Monte Carlo Simulation (MCS) in conjunction with genetic algorithm (GA) and differential evolution (DE) to address uncertainties. The probability density functions (pdf) for total voltage harmonic distortion (UTHD), individual voltage harmonic distortion (UIHDh), and RMS voltage (URMS) have been determined for utilization in chance constrained framework. In addition, the uncertainty effects of PV systems on grid losses for various solar radiation conditions are also investigated. Specifically, the paper aims to evaluate these impacts within the context of stochastic limits. The PV system sizing problem has been addressed inside the distribution system using a chance-constrained framework. A key contribution is the integration of GA, DE, and MCS into a cohesive approach, and the study evaluates the benefits of this approach through an analysis of outcomes derived from the stochastic method. The simulation results illustrate the advantages of the proposed stochastic GA methodology.

Estimation Parameters in the fuzzy class Poisson mixture regression model

In the Poisson mixture regression for the fuzzy class model, observations originate from distinct sub-sources or classes due to the problem of heteroscedasticity. The underlying assumption posits that the observed data stem from a finite mixture of fuzzy classes. The primary challenge lies in achieving the optimal assignment of observations to their respective categories, necessitating the utilization of sophisticated methods for parameter estimation within the model. This research paper explores the estimation of parameters in a Poisson mixture regression model designed for the fuzzy class. Leveraging the FCML (Fuzzy Classification Maximum Likelihood algorithm) and genetic algorithm methodologies, we conducted simulations to assess the accuracy and efficacy of parameter estimation. Our findings reveal a clear superiority of the genetic algorithm over the FCML algorithm, as evidenced by the Mean Square Error (MSE) criterion. The genetic algorithm consistently produced lower MSE values, indicating more precise and reliable parameter estimates compared to the FCML algorithm. This research underscores the potential of the genetic algorithm as a robust and effective tool for parameter estimation in complex statistical models, offering researchers an alternative approach for tackling challenges in the estimation of parameters within the context of the fuzzy class

Enhancing probabilistic based real-coded crossover genetic algorithms with authentication of VIKOR multi-criteria optimization method

<p> To improve the performance of genetic algorithms (GAs) in complex optimization settings, this work offered two novel real-coded crossover operators: one based on the Gumbel distribution (GX) and the other on the Rayleigh distribution (RX). These innovative operators, when combined with three different mutation techniques, created a significant improvement in GA methodology. Our meticulous simulations showed that the GX operator significantly outperformed RX and other traditional operators, demonstrating its superior capacity to address complex optimization problems. The GX operator's unusual robustness was further validated through detailed performance analysis utilizing the VlseKriterijuska Optimizacija I Komoromisno Resenje (VIKOR) multi-criteria decision-making technique, setting a new standard in crossover operator design and significantly improving the state of the art in GAs.</p>

Smart Health Monitoring System of Agricultural Machines: Deep Learning-based Optimization with IoT and AI

Implementing intelligent monitoring systems for Agricultural Machinery (AM) is hindered by the intricate and costly nature of the Internet of Things (IoT) sensor technologies. The heavy reliance on cloud and fog computing, the availability of network infrastructure, and the need for expert knowledge pose challenges in rural areas that lack network connectivity. Using edge devices, such as smartphones, which possess significant computational capabilities, is a potential solution that has not yet been fully realized in the commercial sphere. Furthermore, the increasing demand from users for economically viable and user-friendly technology serves as a driving force for transitioning away from expensive and intricate sensors towards more cost-effective alternatives. In the IoT era, there is anticipated to be a widespread network connection between a vast array of AM and service centers. Using smartphone applications has increased the potential for edge computation on smartphones to significantly aid in network traffic control. The development of an Artificial Intelligence (AI) - -based data analytic method poses a significant challenge due to the need to optimize for the limited computational capabilities of smartphones. However, the users’ demand for affordable technology renders it resistant to easy penetration. This paper uses IoT and AI to propose a Smart Health Monitoring System for Agricultural Machines with Deep Learning-based Optimization (SHMAM-DLO). This paper aims to propose a Fusion Genetic Algorithm (FGA) methodology and Artificial Neural Network (ANN) for optimization during monitoring the health of AM. The proposed approach enables cost-effective utilization of smartphone end devices by leveraging their built-in microphones instead of relying on expensive IoT sensors.

Identification and optimization of material constitutive equations using genetic algorithms

The modeling and simulation of engineering materials using constitutive equations requires a large set of optimized coefficients that characterize the hardening or softening behavior. Optimizing this large set of coefficients with multiple constraints is very challenging using conventional optimization methods. This research proposes a novel model-agnostic framework based on genetic algorithms to identify and optimize the set of coefficients of the constitutive equations of engineering materials. The proposed framework demonstrates solution convergence, scalability to available data, and high explainability over a wide range of engineering materials, including titanium-based, iron-based, and aluminum-based alloys. The experimental test data for necessary validation of the computational framework was generated using the MTS fatigue testing machine equipped with a highly sensitive extensometer. The Chaboche unified visco-plasticity material model has been implemented as an example in this study which can deal with both cyclic effects, such as combined isotropic and kinematic hardening, and rate-dependent effects associated with visco-plasticity. The experimentally obtained cyclic response of three different classes of materials was compared with their optimized simulated constitutive equations, and the results were in good agreement. Furthermore, the proposed multi-objective optimization using genetic algorithm methodology avoids local optimality and converges to the optimal solution much faster than commercially available software.

Water distribution system calibration using the Finite Element Method coupled to a Genetic Algorithm

Water distribution systems (WDS) are a fundamental part of the infrastructure in our cities, for this reason increasingly capable numerical models have been developed in recent years to allow hydraulic systems be analyzed in great detail. Calibration process let get accurate and reliable information for those involved in design, operation, construction and maintenance of public WDS. In this study a calibration process is proposed using a Genetic Algorithms (GA) methodology with real number coding coupled to the Finite Element Method as hydraulic simulator GA-FEMH. The method proposed was applied to two calibration cases; one reported in literature and a real network.The results show that the proposed calibration process is reliable, and the WDS model can represent real conditions of the network according to the regulations that international organizations such as AWWA and WAA recommend. Moreover high correlation values were obtained after the calibration process, with values of R2 higher than 95 % for pressure and flow. Likewise, the FEM can be an alternative to use it as a hydraulic simulator due to its rapid convergence and its ease of integrating hydraulic accessories.

A novel design of evolutionally computing to study the quarantine effects on transmission model of Ebola virus disease

In recent years, hybrid genetic algorithms have attracted a lot of attention and are being utilized more frequently to address real-world issues. A genetic algorithm can combine multiple approaches within its framework to create a hybrid that benefits the most from the combination. In this article, a new methodology of hybrid genetic algorithm using sequential quadratic programming (HGASQP) is presented to optimize the transmission dynamics of the Ebola virus disease model (EVDM) by implementing the intelligent paradigm of feed-forward neural networks. The Ebola virus, also known as Ebola haemorrhagic, is a transmitter virus that was initially transferred to humans by wild and domestic animals, and then it spread from human to human. To control this spread, a mathematical model is proposed consist of susceptible-S, exposed-E, infected-I, quarantined-Q and recovered-R classes. Coupling GA with local search SQP take the advantages of both for determining the global optimum as well as local optimum while at the same time also providing fast convergence. The mean squared error (MSE) is minimized as fitness objective function to optimize HGASQP. The optimized solutions of HGASQP are compared with the numerical results of the Adam approach in order to validate the effectiveness and robustness of the proposed algorithm. Furthermore, statistical and quantitative analysis are also established to authenticate the performance of HGASQP. It has been demonstrated that the suggested optimization approach is precise and can boost optimization effectiveness.

Machine learning-based multi-objective optimization for efficient identification of crystal plasticity model parameters

A set of constitutive model parameters along with crystallography governs the activation of deformation mechanisms in crystal plasticity. The constitutive parameters are typically established by fitting of mechanical data, while microstructural data is used for verification. This paper develops a Pareto-based multi-objective machine learning methodology for efficient identification of crystal plasticity constitutive parameters. Specifically, the methodology relays on a Gaussian processes-based surrogate model to limit the number of calls to a given crystal plasticity model, and, consequently, to increase the computational efficiency. The constitutive parameters pertaining to an Elasto-Plastic Self-Consistent (EPSC) crystal plasticity model including a dislocation density-based hardening law, a backstress law, and a phase transformations law are identified for two materials, a dual phase (DP) steel, DP780, subjected to load reversals and a stainless steel (SS), 316L, subjected to strain rate and temperature sensitive deformation. The latter material undergoes plasticity-induced martensitic phase transformations. The optimization objectives were the quasi static flow stress data for the DP steel case study, while a set of strain-rate and temperature sensitive flow stress and phase volume fraction data for the SS case study. The procedure and results for the two case studies are presented and discussed illustrating advantages and versatility of the developed methodology. In particular, the efficiency of the developed methodology over an existing genetic algorithm methodology is discussed. Additionally, the parameters identified for the SS case study were utilized to simulate three biaxial tensile loading paths using a finite element implementation of EPSC for further verification.

Experimental Investigation and Optimization of Turning Polymers Using RSM, GA, Hybrid FFD-GA, and MOGA Methods.

The machining of polymers has become widely common in several components of industry 4.0 technology, i.e., mechanical and structural components and chemical and medical instruments, due to their unique characteristics such as: being strong and light-weight with high stiffness, chemical resistance, and heat and electricity insolation. Along with their properties, there is a need to attain a higher quality surface finish of machined parts. Therefore, this research concerns an experimental and analytical study dealing with the effect of process parameters on process performance during the turning two different types of polymers: high-density polyethylene (HDPE) and unreinforced polyamide (PA6). Firstly, the machining output responses (surface roughness (Ra), material removal rate (MRR), and chip formation (λc)) are experimentally investigated by varying cutting speed (vc), feed rate (f), and depth of cut (d) using the full factorial design of experiments (FFD). The second step concerns the statistical analysis of the input parameters’ effect on the output responses based on the analysis of variance and 3D response surface plots. The last step is the application of the RSM desirability function, genetic algorithm (GA), and hybrid FFD-GA techniques to determine the optimum cutting conditions of each output response. The lowest surface roughness for HDPE was obtained at vc = 50 m/min, f = 0.01 mm/rev, and d = 1.47 mm and for PA6 it was obtained at vc = 50 m/min, f = 0.01 mm/rev, and d = 1 mm. The highest material removal rate was obtained at vc = 150 m/min, f = 0.01 mm/rev, and d = 1.5 mm for both materials. At f = 0.01 mm/rev, d = 1.5 mm, and vc = 100 for HDPE, and vc = 77 m/min for PA6, the largest chip thickness ratios were obtained. Finally, the multi-objective genetic algorithm (MOGA) methodology was used and compared.

Train Speed Profile Optimization for Energy Saving

This study aims to optimize energy consumption by modifying the train’s maximal speed and coasting velocity. The methods used in the simulation are brute force and genetic algorithm (GA). The introduction briefly introduces the aim and objectives of the study, as well as the scope and the methodology. The following section gives an overview of the current rail transit development and the existing issues. Despite the rapid development of rail transit and its successful operation, energy consumption is a major issue. The methodology of brute force and genetic algorithm is then introduced. The exact algorithm of the two methods in MATLAB is explained so as to make preparations for the latter simulation optimization. The results from the brute force and genetic algorithm methods are obtained and compared for data analysis. The driving strategy for using STS (Single Train Simulator) is then optimized for an advanced modification. By inserting more values in the code, an optimal speed profile is obtained, and the energy saving target is achieved. Overall, the energy consumption of the studied line could be decreased by optimizing the maximal speed of different sections between the stations and the coasting velocity. However, influencing factors such as service and infrastructure, application of acceleration, and braking power should also be considered as improvements in future studies.

Development and performance study of biomedical porous zinc scaffold manufactured by using additive manufacturing and microwave sintering

ABSTRACT Zinc (Zn) and its alloys are gaining popularity day by day in biomedical applications due to their acceptable biocompatibility and moderate degradation rate than magnesium and iron. However, the fabrication procedure for the metallic scaffold is still a challenging task. Hence, the present study aims to establish a new manufacturing route for fabricating Zn scaffold utilizing 3D printing and microwave sintering. The Central composite design (CCD) methodology was used to investigate the influence of sintering parameters like sintering temperature (St), heating rate (Hr), and soaking duration (Sd) on the compressive strength (C.S) & sintered density (S.D) values of the fabricated sample. It was found that the value of C.S and S.D increased with an increase in soaking duration and heating rate. Similar to Hr and Sd, C.S and S.D values increased with increasing sintering temperature, but the values of both properties declined after a critical temperature. Multi-objective optimization was performed utilizing Genetic algorithm (GA) methodology, a tool of MATLAB to obtain the optimum values of process input factors for maximum C.S (18.6MPa) and S.D (6.79 gm/cm3). In the end, a comparative study of the Zn scaffold was performed to evaluate the performance of developed fabrication method, and results were found satisfactory.

A new approach for balanced power distribution in energy sources with minimal fuel consumption on diesel-electric vessels

ABSTRACT This paper presents a new approach to minimize the fuel consumption of diesel-electric vessels using marine diesel generators as their energy source. The suggested method provides equal power distribution between the identical generators, taking into account the optimum fuel consumption resulting from electrical energy generation on board. The proposed approach was applied to a diesel-electric vessel model equipped with four 1900 kW generators and tested for power requirements between 380–6840 kW. Then, the power distribution and fuel consumption behaviors of the generators were examined. To verify the results of the study, the suggested method was compared with the genetic algorithm methodology. The results show that although the power distribution behavior between energy sources is different in both methods, they tend to meet the same power demand with almost similar fuel consumption. In addition, less fuel consumption could be achieved with the proposed approach for power needs in the range of 1520–1900 kW, 3230–3610 kW, and 4560–5320 kW. This means that, thanks to the proposed approach, both the fuel consumption of the generators and the problems that may occur due to the unbalanced power distribution of the electrical system can be minimized.

Re‐examination of the 1928 Parral, Mexico earthquake (M6.3) using a new multiplatform graphical vectorization and correction software for legacy seismic data

AbstractModern analysis of earthquakes depends on digital time series; however, only about 30% of the timespan of recorded seismicity is available in digital format. During the first half of the 20th century, most of the earthquake ground motions in Mexico were recorded by Wiechert mechanical instruments on smoked paper. In this work, we developed and use Tiitba, a new portable multiplatform graphical user interface (GUI) open‐source software coded on python, to vectorize and correct old analogue seismograms. Using this software, we vectorized the 11/1/1928 Parral (M6.3) earthquake seismograms to obtain the constant time‐interval digitized time series for each component and station of the seismogram. Then, we obtained its source focal mechanism using a genetic algorithm methodology. Also, with an auxiliary Tiitba module, we constructed a SEISAN S‐file to relocate the hypocentre of this earthquake. The obtained relocation is about 125 km south of a recently recorded seismic swarm that occurred in 2013, which has a similar focal mechanism to the one that we obtained in this work.

Minimization of completion time variance in flowshops using genetic algorithms

The majority of the flowshop scheduling literature focuses on regular performance measures like makespan, flowtime etc. In this paper a flowshop scheduling problem is addressed where the objective is to minimize completion time variance (CTV). CTV is a non-regular performance measure that is closely related to just-in-time philosophy. A Microsoft Excel spreadsheet-based genetic algorithm (GA) is proposed to solve the problem. The proposed GA methodology is domain-independent and general purpose. The flowshop model is developed in the spreadsheet environment using the built-in formulae and function. Addition of jobs and machines can be catered for without the change in the basic GA routine and minimal change to the spreadsheet model. The proposed methodology offers an easy-to-handle framework whereby the practitioners can implement a heuristic-based optimization tool with the need for advanced programming tools. The performance of the proposed methodology is compared to previous studies for benchmark problems taken from the literature. Simulation experiments demonstrate that the proposed methodology solves the benchmark problems efficiently and effectively with a reasonable accuracy. The solutions are comparable to previous studies both in terms of computational time and solution quality.

Strength-based design of a sunflower stalk cutter machine design using finite element analysis and experimental validation

In this study, the total algorithm of the strength-based design of the system for mass production has been developed. The proposed algorithm, which includes numerical, analytical, and experimental studies, was implemented through a case study on the strength-based structural design and fatigue analysis of a tractor-mounted sunflower stalk cutting machine (SSCM). The proposed algorithm consists of a systematic engineering approach, material selection and testing, design of the mass criteria suitability, structural stress analysis, computer-aided engineering (CAE), prototype production, experimental validation studies, fatigue calculation based on an FE model and experimental studies (CAE-based fatigue analysis), and an optimization process aimed at minimum weight. Approximately 85% of the system was designed using standard commercially available cross-section beams and elements using the proposed algorithm. The prototype was produced, and an HBM data acquisition system was used to collect the strain gage output. The prototype produced was successful in terms of functionality. Two- and three-dimensional mixed models were used in the structural analysis solution. The structural stress analysis and experimental results with a strain gage were 94.48% compatible in this study. It was determined using nCode DesignLife software that fatigue damage did not occur in the system using the finite element analysis (FEA) and experimental data. The SSCM design adopted a multi-objective genetic algorithm (MOGA) methodology for optimization with ANSYS. With the optimization solved from 422 iterations, a maximum stress value of 57.65 MPa was determined, and a 97.72 kg material was saved compared to the prototype. This study provides a useful methodology for experimental and advanced CAE techniques, especially for further study on complex stress, strain, and fatigue analysis of new systematic designs desired to have an optimum weight to strength ratio.

Effective Intrusion Detection System to Secure Data in Cloud Using Machine Learning

When adopting cloud computing, cybersecurity needs to be applied to detect and protect against malicious intruders to improve the organization’s capability against cyberattacks. Having network intrusion detection with zero false alarm is a challenge. This is due to the asymmetry between informative features and irrelevant and redundant features of the dataset. In this work, a novel machine learning based hybrid intrusion detection system is proposed. It combined support vector machine (SVM) and genetic algorithm (GA) methodologies with an innovative fitness function developed to evaluate system accuracy. This system was examined using the CICIDS2017 dataset, which contains normal and most up-to-date common attacks. Both algorithms, GA and SVM, were executed in parallel to achieve two optimal objectives simultaneously: obtaining the best subset of features with maximum accuracy. In this scenario, an SVM was employed using different values of hyperparameters of the kernel function, gamma, and degree. The results were benchmarked with KDD CUP 99 and NSL-KDD. The results showed that the proposed model remarkably outperformed these benchmarks by up to 5.74%. This system will be effective in cloud computing, as it is expected to provide a high level of symmetry between information security and detection of attacks and malicious intrusion.

Optimization of Process‐Control Parameters for the Diameter of Electrospun Hydrophilic Polymeric Composite Nanofibers

Electrospinning In article number 2100471 by Muhammet Emin Cam, Mohan Edirisinghe, and co-workers, water-soluble ternary polymer blends are electrospun. Genetic algorithm methodology is used to optimize process-control parameters; applied voltage, flow rate and working distance, to theoretically and experimentally compare the impact on nanofiber manufacturing in a world seeking environmentally friendly manufacturing and products.

The Most Economical Design of Hybrid PV/Wind/Battery/Diesel Generator Energy System Considering Various Number of Design Parameters Based on Genetic Algorithm

The optimal sizing of a hybrid energy system may be a difficult undertaking problem, because of the huge number of structure settings and the irregular nature of solar radiation and wind power sources. This issue has a place with the classification of combinatorial enhancement, and its solution dependent on the classical technique may be a waste of time. This paper proposes a Genetic Algorithm (GA) methodology to find the optimal sizing of a hybrid Photovoltaic, Wind Turbine, Battery Storage, and Diesel Generators (PV/WT/BA/DG) energy system based on the number of PV modules, the number of wind turbines, the number of batteries, the number of diesel generators, the slope angle of PV panel and a hub height of wind turbine as the design parameters and study its effect on the Levelized Cost of Energy (LCOE). The proposed method aims to minimize the LCOE with high reliability of load supplying by increasing the design variables gradually. The proposed method will be tested at different sites with various metrological data to ensure its robustness. The results show that the LCOE decreases as the design parameters increase, also that the average wind-speed is inversely proportional to the LCOE of the site under study.

Multi-Criteria Optimization of Energy-Efficient Cementitious Sandwich Panels Building Systems Using Genetic Algorithm

This paper presents results of a study that focuses on developing a genetic algorithm (GA) for multi-criteria optimization of orthotropic, energy-efficient cementitious composite sandwich panels (CSP). The current design concept of all commercially produced CSP systems is based on the assumption that such panels are treated as doubly reinforced sections without the consideration of the three-dimensional truss contribution of the orthotropic panel system. This leads to uneconomical design and underestimating both the strength and stiffness of such system. In this study, two of the most common types of commercially produced sandwich were evaluated both numerically and experimentally and results were used as basis for developing a genetic algorithm optimization process using numerical modeling simulations. In order to develop a sandwich panel with high structural performance, design optimization techniques are needed to achieve higher composite action, while maintaining the favorable features of such panels such as lightweight and high thermal insulation. The study involves both linear and nonlinear finite element analyses and parametric optimization. The verification and calibration of the numerical models is based on full-scale experimental results that were performed on two types of commercially produced sandwich panels under different loading scenarios. The genetic algorithm technique is used for optimization to identify an optimum design of the cementitious composite sandwich panels. The GA technique combines Darwin’s principle of survival of fittest and a structured information exchange using randomized crossover operators to evolve an optimum design for the cementitious sandwich panel. Parameters evaluated in the study include: (i) shear connectors’ geometry, its volume fraction and distribution; (ii) exterior cementitious face sheets thickness and (iii) size and geometry steel wires reinforcements. The proposed optimization method succeeded in reducing cost of materials of CSP by about 48% using genetic algorithm methodology. In addition, an optimized design for CSP is proposed that resulted in increasing the panel’s thermal resistance by 40% as compared to existing panels, while meeting ACI Code structural design criteria. Pareto-optimal front and Pareto-optimal solutions have been identified. Correlation between the design variables is also verified and design recommendation are proposed.