Application Of Genetic Algorithm Research Articles

Stock price prediction portfolio optimization using different risk measures on application of genetic algorithm for machine learning regressions

Stock price prediction portfolio optimization using different risk measures on application of genetic algorithm for machine learning regressions

Application of Genetic Algorithm to the Traditional Layout and Spatial Optimization Design of Suzhou Gardens

Application of Genetic Algorithm to the Traditional Layout and Spatial Optimization Design of Suzhou Gardens

Application of Genetic Algorithm for Synthesizing Onedimensional Thinned Antenna Arrays to form Twodimensional Thinned MIMO Antenna Arrays

The possibility of using thinned classical linear antenna arrays to synthesize two-dimensional thinned MIMO antenna arrays on their basis is considered. The MIMO antenna array which receiving and transmitting elements are located on two mutually orthogonal lines is regarded. On the example of this array it is shown that the directivity diagram of its virtual aperture has the property of separability of spatial variables. It allows using the arrangement of elements of a thinned classical linear antenna array for the receiving and transmitting elements lines. It is demonstrated that a thinned MIMO antenna array can form a radiation pattern with a lower level of side lobes than a full array while keeping the width of the main lobe unchanged. The dependence allowing to determine the peak level of side lobes of a twodimensional thinned MIMO antenna array at a given thinning factor is given.

Research on Space Optimization Design of High-rise Residential Building Based on Genetic Algorithm

Abstract With the rapid development of urbanization and the continuous growth of population, the design and planning of high-rise residential buildings have become increasingly important. The purpose of this study is to explore the space optimization design method of high-rise residential buildings based on genetic algorithm(GA), focusing on the comparative analysis between traditional GA and Adaptive genetic algorithm(AGA). In this paper, AGA is used to establish the spatial optimization model of high-rise residential buildings. By dynamically adjusting the parameters of the algorithm, AGA makes the algorithm better adapt to the characteristics of the problem and improves the search efficiency. The results show that AGA is superior to traditional GA in global convergence probability, especially when the population size is large. AGA improves the adaptability and robustness of the algorithm by dynamically adjusting the crossover and mutation probability. AGA has better flexibility and adaptability in the design of high-rise residential buildings and is expected to provide more optimized solutions for solving complex design problems. The findings of this study provide a useful reference for innovation and sustainable development in the field of high-rise building design and also provide practical methods and tools for the application of GA.

Hybrid Technologies and Genetic Algorithms Applied to School Lecture Timetable Generation

Hybrid Technologies and Genetic Algorithms Applied to School Lecture Timetable Generation

Application of response surface methodology and genetic algorithm in the prediction and optimization of the ductile properties of mild steel weld

Welding has been identified as an excellent joining process which guarantees, good ductile joints devoured of failure. However determining the right parameters, and weld process is key to obtaining a quality joint. This study is performed to evaluate the effect of the input process parameters on the responses (tensile strength, percentage elongation, and bending strength), and to optimize the ductile properties of the weld variables using response surface methodology (RSM), and genetic algorithm (GA) techniques. Analysis carried out using the two different techniques showed the suitability of the techniques in obtaining the optimum weld parameters and their significant differences in results. The GA, performed more efficiently with its results recording a less than 6% error. However, the suitability of the RSM, is displayed in its ability to reveal the parameters with the most significant effect. The result shows that the weld current and arc voltage are the parameters with the most significant effect on the ductility of the welded joint, haven recorded p values less than 0.005 in the ANOVA analysis. But for the microscopic images obtained from both the RSM and GA welds, the study shows that the GA parameters obtained are capable of producing a more durable welds.

A Review of Optimization Studies for System Appointment Scheduling

In the face of an increasingly high-demand environment for outpatients, achieving a balance between allocation of limited medical resources and patient satisfaction has considerable social and economic benefits. Therefore, appointment scheduling (AS) system operation is used in clinics and hospitals, and its operation optimization research is of great significance. This study reviews the research progress on appointment scheduling system optimization. Firstly, we classify and conclude the existing appointment scheduling system structures and decision-making frameworks. Subsequently, we summarize the system reliability optimization framework from three aspects: appointment scheduling system optimization objectives, decision variables and constraints. Following that, we methodically review the most applied system optimization algorithms in different appointment scheduling systems. Lastly, a literature bibliometric analysis is provided. During our review of the literature, we observe that (1) optimization methods in ASs predominantly involve the application of genetic algorithms and simulation optimization algorithms; (2) neural networks and deep learning methods are core technologies in health management optimization; (3) a bibliometric analysis reveals a heightened interest in the optimization technology of ASs within China compared to other nations; and (4) further advancements are essential in the comprehensive optimization of the system, exploration of practical usage scenarios, and the application of advanced simulation and modeling techniques in this research.

ГЕНЕТИЧНІ АЛГОРИТМИ В ЗАДАЧАХ ОПТИМІЗАЦІЇ РОБОТИ ФЕС

Solving the tasks of finding the maximum in the field of renewable energy sources. The scientific work is devoted to the application of genetic algorithms to solve the problems of optimizing the efficiency of solar energy in renewable energy sources. The main focus of the work is on finding the maximum parameter values in solar power plants. The relevance of the study is due to the constant growth in the popularity of the use of solar energy and the need to increase its conversion efficiency. The application of genetic algorithms in solving the tasks of finding the maximum in solar energy is an important step in achieving optimal configurations of FES and ensuring the stable functioning of this type of power plants in general. The work includes the analysis of the influence of various parameters on the efficiency of photovoltaic panels and the development of optimal strategies for the use of genetic algorithms to improve their performance. The obtained results open up new opportunities for increasing the competitiveness of FES in the field of renewable energy sources. The genetic algorithm is recognized for its ability to provide quality results and work faster than the selection method. This method is widely used in world practice [6]. Modern algorithms for tasks where the size of the search space is so large that the exact finding of the optimal solution becomes impossible, then in such cases heuristic solutions meet the requirements, have also been studied. One of the goals of the research is the analysis of optimization algorithms and their applicability for solving the optimization tasks of solar energy. Genetic algorithms, although effective, have their limitations - in many cases, they tend to converge to a local optimum (or even an arbitrary point), instead of a global one. This indicates their inability to decide how to maintain high fitness in the short term. Additionally, the complication is related to how to protect evolutionarily formed parts from destructive mutations [7, 9]. In the process of research, the specified limitations were taken into account and mechanisms were developed to reduce their negative impact. The algorithm considered in the work is not only resistant to local minima, but also, due to the internal parallelism expressed in working with individual solutions, rather than whole classes of solutions, provides a relatively fast search for the optimal solution. Research methods basically use the iterative technique of improving results

Application of genetic algorithm for the synthesis of path-generating compliant mechanisms

This study demonstrates a significant achievement in using the Genetic Algorithms technique to design compliant mechanisms capable of tracking a predetermined non-linear output path. The main objective of this research is to develop a computational optimization framework for generating mechanisms that satisfy the specified input requirements with the highest precision. The authors present a tool that incorporates a Python interface and ANSYS MAPDL software for modeling, solving, and optimizing compliant mechanisms. The optimization objective and algorithm are outlined, followed by a report of the results and recommendations for future research directions.

From Patents to Progress: Genetic Algorithms in Harmonic Distortion Monitoring Technology

Sustainable energy sources, such as wind energy, are pivotal in driving our energy landscape towards a more environmentally conscious and responsible future. Wind power, as an exemplar of clean and renewable energy solutions, adeptly harnesses the kinetic energy of the wind to generate electricity. While wind energy significantly contributes to our sustainability objectives, the quality of the energy it produces is equally essential. A critical challenge in this context is harmonic distortion, which manifests as unwanted fluctuations in the frequency and amplitude of electrical waveforms. Effectively mitigating these distortions within wind energy systems is vital to maintaining the stability and reliability of power grids, guaranteeing that the electricity supplied adheres to high-quality standards. The objective of this study was to conduct a technological prospection focused on the contemporary scenario of genetic algorithm applications in addressing harmonic variations. This investigation unearthed a total of 634 relevant documents. The findings suggest that the utilization of genetic algorithms for enhancing energy quality is a relatively recent but promising field. The State Grid Corp of China emerged as the principal contributor, with ten noteworthy inventors identified. Remarkably, both China and the United States lead in patent filings. The insights gleaned from these documents underscore the potential for further exploration and the synergistic application of these techniques. These collaborative efforts have the potential to yield processes and devices that offer significant economic and environmental advantages for the energy industry, solidifying our commitment to a cleaner and more sustainable energy future.

Penerapan Algoritma Genetika Untuk Penjadwalan Mata Pelajaran Di SMK Negeri 1 Kota Tambolaka

consider other things such as avoiding the same or repeated schedules in one day, the number of teaching staff involved, and practicum subjects which in their implementation require a laboratory. The subject scheduling process at SMK Negeri 1 Tambolaka City is still carried out manually by Deputy Head 1 for the curriculum field, who currently uses Microsoft Excel software, which has the potential for errors and often requires correction before the schedule is implemented. Because the subject scheduling process is very important, a solution is needed in making subject schedules. Therefore, this research aims to optimize course schedules by applying a genetic algorithm to the course scheduling application. In this research, the process of preparing rosters for applied subjects was carried out at school using a web-based genetic algorithm method so that schedule collisions did not occur and the process was more optimal. In the experiment, the results of this subject scheduling application system consisted of several forms and menus, including a login form, department data menu, subject data, class data, teachers, hour ranges, special schedules, and the scheduling menu. Creating a teacher's teaching schedule using a genetic algorithm can produce a schedule automatically and systematically, thereby displaying the schedule for the day and time of each teacher's teaching schedule. The application of genetic algorithms in scheduling teacher teaching at SMK Negeri 1 Tambolaka City is a very optimal solution for scheduling, so that the possibility of similarities in the same teacher's teaching time and hours is very small.

Optimizing lot sizing model for perishable bread products using genetic algorithm

This research addresses order planning challenges related to perishable products, using bread products as a case study. The problem is how to effi­ci­ently manage the various bread products ordered by diverse customers, which requires distributors to determine the optimal number of products to order from suppliers. This study aims to formulate the problem as a lot-sizing model, considering various factors, including customer demand, in­ven­tory constraints, ordering capacity, return rate, and defect rate, to achieve a near or optimal solution, Therefore determining the optimal order quantity to reduce the total ordering cost becomes a challenge in this study. However, most lot sizing problems are combinatorial and difficult to solve. Thus, this study uses the Genetic Algorithm (GA) as the main method to solve the lot sizing model and determine the optimal number of bread products to order. With GA, experiments have been conducted by combining the values of population, crossover, mutation, and generation parameters to maximize the feasibility value that represents the minimal total cost. The results obtained from the application of GA demonstrate its effectiveness in generating near or optimal solutions while also showing fast computational performance. By utilizing GA, distributors can effectively minimize wastage arising from expired or perishable products while simultaneously meeting customer demand more efficiently. As such, this research makes a significant contri­bution to the development of more effective and intelligent decision-making strategies in the domain of perishable products in bread distribution.

A Comparative Analysis of Optimization Algorithms for Finite Element Model Updating on Numerical and Experimental Benchmarks

Finite Element Model Updating (FEMU) is a common approach to model-based Non-Destructive Evaluation (NDE) and Structural Health Monitoring (SHM) of civil structures and infrastructures. Its application can be further utilized to produce effective digital twins of a permanently monitored structure. The FEMU concept, simple yet effective, involves calibrating and/or updating a numerical model based on the recorded dynamic response of the target system. This enables to indirectly estimate its material parameters, thus providing insight into its mass and stiffness distribution. In turn, this can be used to localize structural changes that may be induced by damage occurrence. However, several algorithms exist in the scientific literature for FEMU purposes. This study benchmarks three well-established global optimization techniques—namely, Generalized Pattern Search, Simulated Annealing, and a Genetic Algorithm application—against a proposed Bayesian sampling optimization algorithm. Although Bayesian optimization is a powerful yet efficient global optimization technique, especially suitable for expensive functions, it is seldom applied to model updating problems. The comparison is performed on numerical and experimental datasets based on one metallic truss structure built in the facilities of Cranfield University. The Bayesian sampling procedure showed high computational accuracy and efficiency, with a runtime of approximately half that of the alternative optimization strategies.

Bayesian Network Structural Learning Using Adaptive Genetic Algorithm with Varying Population Size

A Bayesian network (BN) is a probabilistic graphical model that can model complex and nonlinear relationships. Its structural learning from data is an NP-hard problem because of its search-space size. One method to perform structural learning is a search and score approach, which uses a search algorithm and structural score. A study comparing 15 algorithms showed that hill climbing (HC) and tabu search (TABU) performed the best overall on the tests. This work performs a deeper analysis of the application of the adaptive genetic algorithm with varying population size (AGAVaPS) on the BN structural learning problem, which a preliminary test showed that it had the potential to perform well on. AGAVaPS is a genetic algorithm that uses the concept of life, where each solution is in the population for a number of iterations. Each individual also has its own mutation rate, and there is a small probability of undergoing mutation twice. Parameter analysis of AGAVaPS in BN structural leaning was performed. Also, AGAVaPS was compared to HC and TABU for six literature datasets considering F1 score, structural Hamming distance (SHD), balanced scoring function (BSF), Bayesian information criterion (BIC), and execution time. HC and TABU performed basically the same for all the tests made. AGAVaPS performed better than the other algorithms for F1 score, SHD, and BIC, showing that it can perform well and is a good choice for BN structural learning.

Application of Bayesian network and genetic algorithm in construction safety evaluation

Developing a safety evaluation model for construction is of utmost importance due to the increasing prevalence of safety issues on construction sites in a rapidly growing sector. Consequently, this research integrates a clonal genetic algorithm (CGA) and a Bayesian network (BN) into the current building information modelling technology for building construction to establish a comprehensive safety evaluation model for building construction. To develop a framework for assessing building safety, this study initially filters the factors impacting building safety through an advanced evolutionary algorithm. Subsequently, a BN is employed to understand the structure and parameters of the model. When compared with both a back-propagation neural network (BPNN) model and a genetic-algorithm-optimised neural network model, the CGA–BPNN model shows a network training error of approximately 0.09%. Additionally, the target error value is observed to be around 0.02%, and the genetic cross-over probability of the CGA–BPNN model amounts to 0.6629. These results indicate the small algorithm error and appropriate training time of the model, as well as higher accuracy. The CGA–BPNN model filters the evaluation indexes in the BN and assigns appropriate weights to assess accurately the safety status of the construction project.

Applications of genetic algorithm in prediction of the best achievable combination of hardness and tensile strength for graphene reinforced magnesium alloy (AZ61) matrix composite

Due to lack of knowledge about role of process variables in the hot rolling of magnesium alloy composites, the most desirable wrought products of magnesium alloy-based composites could not be successfully produced till date. Moreover, use of wrought graphene reinforced magnesium alloy-based composite has emerged as a lucrative option for developing novel light alloy products in structural applications. The implicative need to obtain the best possible properties of the composite dictates to fix the optimized process variables in rolling operation. 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 Genetic Algorithm (GA) optimizing tool in association with linear regression, so as to achieve the best combination of hardness and tensile strength of AZ-61-graphene nanoplate (GNP) composite.Modified magnesium AZ-61 alloy composites of varying GNP content (0, 1 and 2 wt.%) are produced by stir casting. The cast composites are homogenized at 450 °C for 12 h, followed by iced water quenching. The quenched materials are subjected to hot rolling and the process variables of hot rolling operation consist of rolling temperature, number of rolling passes as well as the initial stock thickness. The rolled materials are tested for hardness and tensile strength as the response variables. Instead of using artificial neural network to fix the objective function of GA, a mathematical model is developed by regression analysis; the derived equation is used as the objective function for multi-objective optimization task. The present paper explores the usefulness of implementing evolutionary algorithm (as in genetic algorithm) to correctly forecast the effect of rolling process variables onto the hardness and tensile strength of the stir cast AZ alloy-GNP nanocomposite.The experimental results show that the maximum achievable hardness value happens to be 106.6 VHN and is very close to that predicted by GA, ∼107.41VHN. Similarly, the maximum experimental tensile strength is found to be 266.82 MPa whereas, the corresponding GA predicted value is seen to be 253 MPa. A close fit can thus be noticed between the GA predicted and the experimentally obtained values. Barring a couple of exceptions, the difference between the experimental and GA predicted data lies within 1 % to 5 % (based on [Experimental value ∼ predicted value]/ [experimental value]). This entices to believe that the adopted muti-objective optimization strategy is quite useful in determining the optimum combination of rolling parameters that aids in achieving the most desirable property combination.

Energy consumption analysis of power grid distribution transformers based on an improved genetic algorithm

With the promotion of energy transformation, the utilization ratio of electrical power is progressively rising. Since electrical power is challenging to store, real-time production and consumption become imperative, imposing significant demands on the dependability and operational efficiency of electrical power apparatus. Suppose the load distribution among multiple transformers within a transformer network exhibits inequality. In such instances, it will amplify the total energy consumption during the voltage conversion process, and local, long-term high-load transformer networks become more susceptible to failures. In this article, we scrutinize the matter of transformer energy utilization in the context of electricity transmission within grid systems. We propose a methodology grounded on genetic algorithms to optimize transformer energy usage by dynamically redistributing loads among diverse transformers based on their operational status monitoring. In our experimentation, we employed three distinct approaches to enhance energy efficiency. The experimental findings evince that this approach facilitates swifter attainment of the optimal power level and diminishes the overall energy consumption during transformer operation. Moreover, it exhibits a heightened responsiveness to fluctuations in power demand from the electrical grid. Experimental results manifest that this technique can truncate monitoring time by 27% and curtail the overall energy consumption of the distribution transformer network by 11.81%. Lastly, we deliberate upon the potential applications of genetic algorithms in the realm of power equipment management and energy optimization issues.

The application of interactive genetic algorithm in the optimization of medical device product form design

In order to solve the problem of low efficiency in the form design of medical device products and the inability to meet the needs of users, the research took the gastrointestinal machine of medical device as the research object, extracted the form design genes and semantic genes of medical device products, and constructed the form optimization design model of medical device products based on IGA algorithm. The research took the researcher and user data of the medical device design department as the main data, through crossover, mutation. The satisfactory solution is obtained by genetic process such as selection. Satisfactory solutions were obtained through genetic processes such as crossover, mutation and selection. The 12th generation project with the code of 000011 010001 100000 met the optimal solution, and the consensus satisfaction was 0.829. The research and design model can efficiently and automatically provide solutions for the design optimization of medical devices. It is recommended that the design department send out the best solution of the model, and optimize the solution in more detail again to improve the efficiency and accuracy of function realization.

Proposal and exergetic/net present value optimization of a novel integrated process into a two-stage geothermal flash cycle, involving bi-evaporator refrigeration unit: Application of non-dominated sorting genetic algorithm (NSGA-II+LINMAP) method

Considering the benefits of geothermal energy in low-temperature combined processes, this study intends to present, examine, and optimize a novel thermal integration process incorporated into a two-stage geothermal flash cycle. In order to meet this objective, a modified bi-evaporator refrigeration technology using an ejector has been designed, with the capability of generating coolant for air-conditioning and industrial requirements. Furthermore, the entire system utilizes a low-temperature water electrolysis process for hydrogen production. In addition to designing a new process, this study incorporates advanced methodologies to comprehensively analyze the proposed system's thermodynamic, economic, and exergoenvironmental aspects. In this context, an extensive parametric study is undertaken, followed by the definition of two distinct multi-objective optimization scenarios: the energy-environment and the energy-cost scenarios. Based on the outcomes derived from the first scenario, the corresponding objective functions, namely energy efficiency and exergoenvironmental index, are determined to be 52.89% and 0.5, respectively. This condition is also responsible for generating net output electricity, cooling load, and hydrogen production rate of 3.897 MW, 26.88 MW, and 4.02 kg/h, respectively. Moreover, it exhibits an exergy destruction rate of 5493 kW. In the second scenario, the objective functions, i.e., energetic efficiency and payback period, are calculated to be 39.37% and 3.94 years, respectively. The aforementioned products demonstrate capacities of 3.697 MW, 17.66 MW, and 5.0 kg/h, respectively. Besides, the exergy destruction rate is determined to be 4667 kW.

Parametrizing analog multi-compartment neurons with genetic algorithms

Background: Finding appropriate model parameters for multi-compartmental neuron models can be challenging. Parameters such as the leak and axial conductance are not always directly derivable from neuron observations but are crucial for replicating desired observations. The objective of this study is to replicate the attenuation behavior of an excitatory postsynaptic potential (EPSP) traveling along a linear chain of compartments on the analog BrainScaleS-2 neuromorphic hardware platform. Methods: In the present publication we use genetic algorithms to find suitable model parameters. They promise parameterization without domain knowledge of the neuromorphic substrate or underlying neuron model. To validate the results of the genetic algorithms, a comprehensive grid search was conducted. Trial-to-trial variations in the analog system are counteracted utilizing spike-triggered averaging. Results and conclusions: The algorithm successfully replicated the desired EPSP attenuation behavior in both single and multi-objective searches illustrating the applicability of genetic algorithms to parameterize analog neuromorphic hardware.