Adaptive Mutation Genetic Algorithm Research Articles

Optimization of 5G base station coverage based on self-adaptive mutation genetic algorithm

In communication network planning, a rational base station layout plays a crucial role in improving communication speed, ensuring service quality, and reducing investment costs. To address this, the article calibrated the urban microcell (UMa) signal propagation model using the least squares method, based on road test data collected from three distinct environments: dense urban areas, general urban areas, and suburbs. With the calibrated model, a detailed link budget analysis was performed on the planning area, calculating the maximum coverage radius required for a single base station to meet communication demands, and accordingly determining the number of base stations needed. Subsequently, this article proposed the Adaptive Mutation Genetic Algorithm (AMGA) and formulated a mathematical model for optimizing 5G base station coverage to improve the base station layout. Simulation experiments were conducted in three different scenarios, and the results indicate that the proposed AMGA algorithm effectively enhances base station coverage while reducing construction costs, thoroughly demonstrating the value of base station layout optimization in practical applications.

A low speed piezoelectric actuator with high displacement smoothness and its multi-objective optimized method

Piezoelectric actuators are widely utilized in the precision industry field, but the existing piezoelectric actuators are difficult to achieve high displacement smoothness at low speed, which limits the practical application of the piezoelectric actuators. This work proposes a piezoelectric actuator that can achieve high displacement smoothness through a multi-leg coordinated actuation principle. A multi-objective optimized method based on adaptive mutation genetic algorithm is utilized to design the driving leg by comprehensively considering five design variables. The design goal of the maximum displacement of the driving foot and the minimum size of the driving leg are achieved. A prototype is fabricated and the characteristics are tested. The experimental results demonstrate the effectiveness of the optimized method. High displacement smoothness at low speed is also achieved. The output displacements of the proposed actuator at different driving voltages and frequencies show no regression and intermittent motion. The linearity R 2 could reach 0.9978, and the low output speed of the actuator could reach 22.69 µrad s−1 at voltage of 100 Vp–p and frequency of 1 Hz.

Effectiveness of Network Classroom Teaching Based on Genetic Algorithm

In online classroom teaching, the function of teaching system can play an important role in the effectiveness of classroom teaching. How to use genetic algorithm to optimize online classroom teaching system has become a research hotspot. Based on genetic algorithm, this paper proposes an adaptive genetic algorithm model based on the traditional algorithm. After setting the appropriate mutation probability, the model can improve the convergence speed. Moreover, based on adaptive genetic algorithm, combined with the direct value method and BT neural network theory, this paper constructs the online classroom teaching quality evaluation model and the teaching system test paper data model, and optimizes adaptive mutation genetic algorithm and BP neural network to evaluate the teaching effectiveness. Simulation experiments are carried out based on the algorithm model, and the visual parameter values are obtained. After experimental comparison, the initial value of the mutation rate is set between 0.002 and 0.004. For the network classroom teaching system, this paper introduces the system demand analysis, function module design, and database design in detail. Finally, through the questionnaire survey, this paper understands the network situation of students in class and the use of online classroom teaching platform in detail, analyzes the problems and influencing factors of online teaching, and finally puts forward the strategies to improve the effectiveness of online classroom teaching.

Teaching Analysis for Visual Communication Design with the Perspective of Digital Technology.

The turn of contemporary visual culture has led to the expansion of the connotation and scope of visual communication design (VCD) education, the generation of new artistic concepts and forms, and the great changes in the subject education system. VCD instruction now has an enhanced teaching environment and operational platform because of the rapid advancement of digital technology. Digital technology is expected to break through traditional learning methods in the future and will be more widely integrated into VCD courses. A topic that must be addressed and explored in the reform and growth of VCD education is how to build a fairer and more inclusive college art education subject system. Therefore, it is particularly important to design a complete VCD teaching evaluation system. In this paper, artificial intelligence technology is applied to the teaching quality evaluation (TQE) system, and a scientific and reliable TQE model is obtained. The main works of this paper are as follows: (1) analyze the background and significance of TQE research, and systematically expound the domestic and foreign research status of TQE, genetic algorithm, and neural network. (2) Using an adaptive mutation evolutionary method, this research builds a TQE system for the VCD course and produces a BPNN model. The adaptive mutation genetic algorithm's convergence speed is considerably faster than the regular genetic algorithms, the optimized neural network's performance is also superior, and the model has a faster convergence time and better prediction accuracy.

Study on the Quality Analysis and Improvement of Tennis Teaching under the Internet System

Improving the quality of tennis teaching and learning is an important part of the modernization of education in China. The limitations of traditional tennis teaching quality evaluation methods have made them controversial, and it is crucial to improve the scientific, rational, and timely evaluation of tenn is teaching quality. Therefore, it is necessary to establish a scientific and rational quality evaluation model for tennis education to evaluate the quality of tennis education. Based on the principle of constructing a perfect tennis teaching quality evaluation system, this paper analyzes the advantages and disadvantages of the previous tennis teaching quality evaluation methods and summarizes the problems existing in the current tennis teaching quality evaluation system in a university. On this basis, to break the limitations of the existing tennis teaching quality evaluation system in a university, a tennis teaching quality evaluation model based on genetic algorithm (GA) and back-propagation (BP) neural network is proposed and a more scientific reasonable tennis teaching quality evaluation system. The evaluation results of BP neural network optimization method based on adaptive mutation genetic algorithm are very satisfactory.

Music Teaching Management and Music Culture Communication Environment Based on Ergonomics.

Music education has a relationship that is mutually restraining and interdependent. The necessity of spreading music culture offers music education social relevance and existential value in the history of music culture's growth, and the method of spreading music culture—music education—injects strength and life into the development of music culture. Music pedagogy is constrained by the evolution of music culture as a whole. In terms of music pedagogy, traditional culture and their peculiarities or qualities will have an impact on the creation of the curriculum content system, which is also the main topic of music teaching management. By using anthropometry, physiological and psychological measurement, etc. with the human body as the main body, ergonomics is the study of the reasonable coordination between the structure and function of the human body, psychology, mechanics, and music teaching methods in order to satisfy people's physical and mental activities and achieve the best use efficiency. The focus should be on comfort, great performance, safety, and health. In order to create a model for evaluating the quality of music instruction, a back propagation neural network (BPNN) is optimised in this paper using an adaptive mutation genetic algorithm. According to the research, our approach outperforms the BPNN model optimised using conventional evolutionary algorithm by 20.21%. The convergence pace is 80.11% faster than the convergence speed of a conventional genetic algorithm. The comparative result demonstrates that the BPNN teaching quality evaluation model with genetic algorithm optimization based on adaptive mutation can produce a more logical scientific evaluation result for the quality of music instruction.

Design optimization for protective shell of hydrogen cylinder for vehicle based on NPR structure

Aiming at reducing the energy absorption of the hydrogen cylinder for fuel cell vehicles in collision, which may lead to fierce explosion, this paper introduces an energy-absorbing protective shell combining with negative Poisson’s ratio (NPR) structure to the hydrogen cylinder. It adopts three honeycomb structures of the same mass and three kinds of aluminum alloy to determine the optimal structure and material scheme of the protective shell. Besides, sensitivity analysis is selected to screen the design parameters of the optimal structure scheme. Based on this, the overall mass of the designed protective shell, the specific energy absorption of NPR structure, the maximum impact force of rigid wall, and the maximum stress of the liner are taken to establish the optimization mathematical model. Furthermore, adaptive mutation genetic algorithm (AMGA), non-dominated sorting genetic algorithm-ii (NSGA-II), and adaptive simulated annealing algorithm (ASA) are exerted as the optimization algorithms separately to conduct optimization design and a comparison is made between the optimization results. The results show that the optimized energy-absorbing protective shell has better crashworthiness and energy absorption effect, which can effectively improve the collision safety of the hydrogen cylinder. It provides a new idea for the protection design of hydrogen storage system in fuel cells in the future.

The improved genetic and BP hybrid algorithm and neural network economic early warning system

Economic early warning is the recognition and judgment of the state of economic operation, and its research results directly affect the rational formulation of macro-control policies. However, the traditional early warning methods are mainly based on expert experience or simple statistical model, which are difficult to reflect the nature of highly nonlinear economic system and can not meet the objective requirements of macroeconomic early warning. Based on the above background, the purpose of this study is to design an economic early warning system based on improved genetic and BP hybrid algorithm and neural network. Based on the overview of macroeconomic early warning at home and abroad, this study expounds the design of early warning index system, early warning model, establishment of early warning system and other issues in the macroeconomic early warning theoretical system; deeply analyses the theoretical methods of BP neural network and adaptive mutation genetic algorithm, and discusses the feasibility of realizing macroeconomic early warning by BP neural network and adaptive mutation genetic algorithm, The improved genetic and BP hybrid algorithm and neural network economic early warning model are established. Finally, the experimental results show that the correlation coefficient between the composite index and the comprehensive early warning is 0.89, and the delay number is 0, which shows that the early warning index obtained by the early warning system can accurately reflect the actual economic fluctuations. The results show that the improved genetic and BP hybrid algorithm and neural network economic early warning system are effective, feasible and have good accuracy.

4G LTE Network Coverage Optimization Using Metaheuristic Approach

The main focus of this paper is to optimize the coverage of each 4G LTE network cell within the service area. There are many algorithms can be implemented to determine the optimal 4G LTE coverage area including the deterministic and heuristic approaches. The deterministic approach could solve accurately the optimization problem but need more resources and time consuming to determine the convergence parameters. Therefore, the heuristic approaches were introduced to improve the deterministic approach drawback. The methods used are the Differential Evolution Algorithm (DEA) and Adaptive Mutation Genetic Algorithm (AMGA), which are categorized as metaheuristic approach. The DEA and AMGA algorithms have been widely used to solve combinatorial problems, including for solving the network optimizations. In the network optimization, coverage is strongly related to 2 objectives, which are reducing the black spot area and decreasing the overlapping coverage areas. Coverage overlap is a condition when some cell sites in an area overlap. It implies in the occurrence of hand off and an inefficient network management. This research aims to obtain an optimal 4G LTE network coverage and reduce the overlapping coverage areas based on effective e-Node B arrangements by using the DEA and AMGA algorithms. The simulations results showed that the DEA algorithm’s coverage effectiveness was 23,4%, and the AMGA Algorithm’s was 16,32%.

Economic optimisation of range-extended electric bus based on AMGA algorithm

The power of a range-extended electric bus comes from its battery and range-extender. How to design the range-extender working point for the vehicle in the process of running is the key factor to achieve energy conservation and emission reduction. To solve this problem, a vehicle model was built by using AVL Cruise simulation software. Through Cruise and Isight co-simulation optimisation, a multi-objective optimisation model for per 100-km fuel consumption and pollutant emission is established. Optimal variables include upper and lower limits of the power unit and working point of the range-extender. Adaptive mutation genetic algorithm (AMGA) was used as optimisation algorithm. Results showed that fuel consumption and pollutant emissions were effectively reduced. The per 100-km fuel consumption decreased by 48.0%, carbon monoxide emission decreased by 49.6%, hydrocarbon emission decreased by 47.28%, and nitrogen oxide emission decreased by 51.1%. The economics of range-extended electric bus have been greatly improved.

Investigation on optimal path for submarine search by an unmanned underwater vehicle

Unmanned underwater vehicle (UUV) is one of the significant equipment for underwater anti-submarine warfare. In this paper, the optimal anti-submarine search path of a UUV is investigated through maximizing the cumulative detection probability (CDP). The mathematical programming model for optimal UUV search path is established by utilizing an adaptive mutation genetic algorithm (AMGA). The enemy submarine is described as a Markovian target. The search radius and search width of the UUV are considered. In simulation analysis, an approximately logarithmic spiral path is found. Moreover, the influence of different parameters, such as detection distance, different initial distance, different detection velocity, and different escape velocity, on the CDP is revealed. The results indicate that the optimal UUV search path is effective and suggestive for anti-submarine operation.

Optimasi Routing pada Metropolitan Mesh Network Menggunakan Adaptive Mutation Genetic Algorithm

In dynamic and wide networks, such as Metropolitan Mesh Network (MMN), routing becomes very complex because a packet can be blocked before it reaches its destination. In addition, users can also log in or log out from network topology. Therefore, a good routing algorithm, which is able to reduce time in network update process or when there is an error in the network, are required. Routing problems can be represented as the shortest path problem to facilitate completion. In this paper, a routing algorithm optimization using Adaptive Mutation Genetic Algorithm (AMGA) on MMN is presented by determining a probability of 0.000005782 at the beginning, with crossover probability of 0.000847, to reduce or avoid premature convergence.

Transmit Antenna Subset Selection in MIMO OFDM System using Adaptive Mutuation Genetic Algorithm

Multiple input multiple output techniques are considered attractive for future wireless communication systems, due to the continuing demand for high data rates, spectral efficiency, suppress interference ability and robustness of transmission. MIMO-OFDM is very helpful to transmit high data rate in wireless transmission and provides good maximum system capacity by getting the advantages of both MIMO and OFDM. The main problem in this system is that increase in number of transmit and receive antennas lead to hardware complexity. To tackle this issue, an effective optimal transmit antenna subset selection method is proposed in paper with the aid of Adaptive Mutation Genetic Algorithm (AGA). Here, the selection of transmit antenna subsets are done by the adaptive mutation of Genetic Algorithm in MIMO-OFDM system. For all the mutation points, the fitness function are evaluated and from that value, best fitness based mutation points are chosen. After the selection of best mutation points, the mutation process is carried out, accordingly. The implementation of proposed work is done in the working platform MATLAB and the performance are evaluated with various selection of transmit antenna subsets. Moreover, the comparison results between the existing GA with mutation and the proposed GA with adaptive mutation are discussed. Hence, using the proposed work, the selection of transmit antenna with the maximum capacity is made and which leads to the reduced hardware complexity and undisturbed data rate in the MIMO-OFDM system

Comparison of Adaptive Mutation Genetic Algorithm and Genetic Algorithm for Transmit Antenna Subset Selection in MIMO- OFDM

Multiple input multiple output techniques are considered attractive for future wireless communication systems, due to the continuing demand for high data rates, spectral efficiency, suppress interference ability and robustness of transmission. MIMO-OFDM is very helpful to transmit high data rate in wireless transmission and provides good maximum system capacity by getting the advantages of both MIMO and OFDM. The main problem in this system is that increase in number of transmit and receive antennas lead to hardware complexity. To tackle this issue, an effective optimal transmit antenna subset selection method is proposed in paper with the aid of Adaptive Mutation Genetic Algorithm (AGA). Here, the selection of transmit antenna subsets are done by the adaptive mutation of Genetic Algorithm in MIMO-OFDM system. For all the mutation points, the fitness function are evaluated and from that value, best fitness based mutation points are chosen. After the selection of best mutation points, the mutation process is carried out, accordingly. Moreover, the comparison results between the GA with mutation and our GA with adaptive mutation are discussed. Hence, using the proposed work, the selection of transmit antenna with the maximum capacity is made and which leads to the reduced hardware complexity and undisturbed data rate in the MIMO-OFDM system General Terms Signal to noise ratio, Ergodic Capacity, Fitness Function