Network Coding Optimization Method Research Based on Genetic Algorithm

2014 ◽  
Vol 644-650 ◽  
pp. 2059-2062
Author(s):  
Hong Yan Yan
Keyword(s):  
Genetic Algorithm ◽  
Network Coding ◽  
Optimization Method ◽  
Premature Convergence ◽  
Initial Population ◽  
Running Time ◽  
Coding Scheme ◽  
Algorithm Search

Network coding optimization method research based on genetic algorithm applies network coding technology in monophyletic multicast network. After reaching network multicast rate, find link coding scheme which makes the minimum of the total number of network coding. Moreover, it makes the analysis and improvement for general genetic algorithm’s defects in network coding link optimization such as rare individual successful decoded by randomly generated initial population strategies, reduced algorithm search ability, premature convergence of genetic algorithm and long algorithm running time.

scholarly journals Nonlinear Predictive Control for a Boiler–Turbine Unit Based on a Local Model Network and Immune Genetic Algorithm

2019 ◽  
Vol 11 (18) ◽  
pp. 5102
Author(s):  
Hongxia Zhu ◽  
Gang Zhao ◽  
Li Sun ◽  
Kwang Y. Lee
Keyword(s):  
Genetic Algorithm ◽  
Predictive Control ◽  
Turbine Unit ◽  
Optimization Method ◽  
System Stability ◽  
Local Model ◽  
Initial Population ◽  
Immune Genetic Algorithm ◽  
Model Network ◽  
Local Model Network

This paper proposes a nonlinear model predictive control (NMPC) strategy based on a local model network (LMN) and a heuristic optimization method to solve the control problem for a nonlinear boiler–turbine unit. First, the LMN model of the boiler–turbine unit is identified by using a data-driven modeling method and converted into a time-varying global predictor. Then, the nonlinear constrained optimization problem for the predictive control is solved online by a specially designed immune genetic algorithm (IGA), which calculates the optimal control law at each sampling instant. By introducing an adaptive terminal cost in the objective function and utilizing local fictitious controllers to improve the initial population of IGA, the proposed NMPC can guarantee the system stability while the computational complexity is reduced since a shorter prediction horizon can be adopted. The effectiveness of the proposed NMPC is validated by simulations on a 500 MW coal-fired boiler–turbine unit.

Optimal Sensor Placement Based on MAC and SPGA Algorithms

2012 ◽  
Vol 594-597 ◽  
pp. 1118-1122 ◽  
Author(s):  
Yong Ming Fu ◽  
Ling Yu
Keyword(s):  
Genetic Algorithm ◽  
Genetic Manipulation ◽  
Optimization Method ◽  
Frame Structure ◽  
Initial Population ◽  
Improved Genetic Algorithm ◽  
Modal Assurance Criterion ◽  
Sensor Optimization ◽  
Computation Efficiency ◽  
The Individual

In order to solve the problem on sensor optimization placement in the structural health monitoring (SHM) field, a new sensor optimization method is proposed based on the modal assurance criterion (MAC) and the single parenthood genetic algorithm (SPGA). First, the required sensor numbers are obtained by using the step accumulating method. The SPGA is used to place sensors, in which the binary coding is adopted to realize the genetic manipulation through gene exchange, gene shift and gene inversion. Then, the method is further simplified and improved for higher computation efficiency. Where, neither the individual diversity of initial population nor the immature convergence problem is required. Finally, a numerical example of 61 truss frame structure is used to assess the robustness of the proposed method. The illustrated results show that the new method is better than the improved genetic algorithm and the step accumulating method in the search capacity, computational efficiency and reliability.

scholarly journals Using Parallel Genetic Algorithms for Estimating Model Parameters in Complex Reactive Transport Problems

Processes ◽  
2019 ◽  
Vol 7 (10) ◽  
pp. 640 ◽  
Author(s):  
Jagadish Torlapati ◽  
T. Prabhakar Clement
Keyword(s):  
Genetic Algorithm ◽  
Reactive Transport ◽  
Numerical Solutions ◽  
Optimization Method ◽  
Water Quality Modeling ◽  
Model Parameters ◽  
Initial Population ◽  
Desktop Computer ◽  
Linear Speedup ◽  
Transport Problems

In this study, we present the details of an optimization method for parameter estimation of one-dimensional groundwater reactive transport problems using a parallel genetic algorithm (PGA). The performance of the PGA was tested with two problems that had published analytical solutions and two problems with published numerical solutions. The optimization model was provided with the published experimental results and reasonable bounds for the unknown kinetic reaction parameters as inputs. Benchmarking results indicate that the PGA estimated parameters that are close to the published parameters and it also predicted the observed trends well for all four problems. Also, OpenMP FORTRAN parallel constructs were used to demonstrate the speedup of the code on an Intel quad-core desktop computer. The parallel code showed a linear speedup with an increasing number of processors. Furthermore, the performance of the underlying optimization algorithm was tested to evaluate its sensitivity to the various genetic algorithm (GA) parameters, including initial population size, number of generations, and parameter bounds. The PGA used in this study is generic and can be easily scaled to higher-order water quality modeling problems involving real-world applications.

scholarly journals Interactive genetic algorithm based on typical style for clothing customization

2020 ◽  
Vol 15 ◽  
pp. 155892502092003
Author(s):  
Xinjuan Zhu ◽  
Xuefei Li ◽  
Yifan Chen ◽  
Jingwei Liu ◽  
Xueqing Zhao ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Algorithm Design ◽  
Design System ◽  
Initial Population ◽  
Interactive Genetic Algorithm ◽  
Coding Scheme ◽  
Binary Coding ◽  
Speed Up ◽  
Previous Design ◽  
Design Requirements

Companies find it extremely difficult to predict consumers’ needs and requirements, since the spiritual significance of clothing is getting more and more attention. However, most current clothing customization platforms only allow customers to retrieve previous design components from the database and recombine them together, ignoring the customer’s personalized design requirements. In view of the above issues, an intelligent design approach of personalized customized clothing based on typical style and interactive genetic algorithm is proposed in this article. It could generate new fashion styles according to simple customer evolution. The binary coding scheme of suit coat style is presented. And an automatic suit coat design system based on interactive genetic algorithm is developed, in which 10 typical suit coats are selected as the initial population. The experimental results show that the system can alleviate customers’ fatigue and speed up convergence compared with the classic interactive genetic algorithm design, and the designed styles can better meet customers’ preferences.

Application of Genetic Algorithm on Travelling Salesman Person

2014 ◽  
Vol 1048 ◽  
pp. 526-530
Author(s):  
Sambourou Massinanke ◽  
Chao Zhu Zhang
Keyword(s):  
Genetic Algorithm ◽  
Travelling Salesman Problem ◽  
Optimization Method ◽  
Initial Population ◽  
Travelling Salesman ◽  
Minimum Path ◽  
Minimum Number ◽  
Permutation Problem ◽  
The City ◽  
Survival Of The Fittest

GA (Genetic algorithm) is an optimization method based on operators (mutation and crossover) utilizing a survival of the fittest idea. They are utilized favorably in various problems. (TSP) Travelling salesman problem is one of the famous studied. TSP is a permutation problem in which the aim is to determine the shortest tour between n different points (cities), otherwise, the problem aims to find a route covering all cities where that the total distance is minimal. In this study a single salesman travels to each of the cities and close the loop by returning to the city he started, the aim of this study is to determine the minimum number of generations in which salesman does the minimum path, cities are chosen at random as initial population. The new generations are then created iteratively till the proper path is attained.

scholarly journals A genetic algorithm for composing music

2010 ◽  
Vol 20 (1) ◽  
pp. 157-177 ◽  
Author(s):  
Dragan Matic
Keyword(s):  
Genetic Algorithm ◽  
Genetic Material ◽  
Premature Convergence ◽  
Initial Population ◽  
Musical Score ◽  
Genetic Operators ◽  
Suboptimal Solutions ◽  
Starting Solutions

In this paper, a genetic algorithm for making music compositions is presented. Position based representation of rhythm and relative representation of pitches, based on measuring relation from starting pitch, allow for a flexible and robust way for encoding music compositions. This approach includes a pre-defined rhythm applied to initial population, giving good starting solutions. Modified genetic operators enable significantly changing scheduling of pitches and breaks, which can restore good genetic material and prevent from premature convergence in bad suboptimal solutions. Beside main principles of the algorithm and methodology of development, in this paper the analysis of solutions in general is also presented, as well as the analysis of the obtained solutions in relation to the key parameters. Some solutions are presented in the musical score.

scholarly journals Multiclass Interactive Martial Arts Teaching Optimization Method Based on Euclidean Distance

2022 ◽  
Vol 2022 ◽  
pp. 1-10
Author(s):  
Zhechun Hu ◽  
Yunxing Wang
Keyword(s):  
Martial Arts ◽  
Euclidean Distance ◽  
Evolutionary Process ◽  
Poor Performance ◽  
Optimization Method ◽  
Learning Ability ◽  
Initial Population ◽  
Algorithm Optimization ◽  
Learning Groups ◽  
Running Time

Aiming at the problems of low optimization accuracy, poor optimization effect, and long running time in current teaching optimization algorithms, a multiclass interactive martial arts teaching optimization method based on the Euclidean distance is proposed. Using the K-means algorithm, the initial population is divided into several subgroups based on the Euclidean distance, so as to effectively use the information of the population neighborhood and strengthen the local search ability of the algorithm. Imitating the school's selection of excellent teachers to guide students with poor performance, after the “teaching” stage, the worst individual in each subgroup will learn from the best individual in the population, and the information interaction in the evolutionary process will be enhanced, so that the poor individuals will quickly move closer to the best individuals. According to different learning levels and situations of students, different teaching stages and contents are divided, mainly by grade, supplemented by different types of learning groups in the form of random matching, so as to improve the learning ability of members with weak learning ability in each group, which effectively guarantees the diversity of the population and realizes multiclass interactive martial arts teaching optimization. Experimental results show that the optimization effect of the proposed method is better, which can effectively improve the accuracy of algorithm optimization and shorten the running time of the algorithm.

scholarly journals Optimalisasi Distribusi Harga Tiket Pesawat berdasarkan Kepadatan Rute Menggunakan Algoritma Genetika

2019 ◽  
Vol 5 (2) ◽  
Author(s):  
Sri Hutamy Novianti ◽  
Esmeralda C. Djamal ◽  
Agus Komarudin
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Optimization Method ◽  
Aviation Industry ◽  
Initial Population ◽  
Insertion Technique ◽  
The Past ◽  
The Difference ◽  
Ticket Prices ◽  
Compatibility Function

The development of the aviation industry in Indonesia in the past decade has risen sharply. One of the impacts of the development of the aviation industry was the presence of a multilevel tariff concept. Where, the concept is the variation in ticket prices in one class with slightly different facilities such as the difference in penalty fees for making refunds and rebooking. The concept of multilevel rates is usually referred to as sub-class rates. One application of the sub-class tariffs in economic classes is divided into four types of sub-classes special promo sub-classes, promo sub-classes, then affordable sub-class and flexible sub-class. One optimization method of getting a combination that meets the requirements without having to try all possibilities is the Genetic Algorithm. The chromosomes built represent 10 subclasses on 9 routes so that they have 90 genes. The use of genetic algorithms originated from the generation of an initial population of 8 chromosomes with a length of 90 genes performed randomly, evaluation of the compatibility function was then selected using the Rank based fitness technique, crosses using Multi-Point Crossover, mutations with the Mutation Insertion technique. The system built was tested with two conditions each of eight tests with 100 generations. First, the test uses the mutation method of three subclass codes on four routes at a capacity of 150 seats, obtained the largest match value of Rp. 750,752,200 and the smallest Rp. 662,283,100. And testing with the mutation method of three subclass codes on eight routes of 150 seat capacity obtained the largest match value of Rp. 763,265,300 and the smallest Rp. 547,396,200. The results of testing the mutation method on eight routes resulted in a higher match value compared to the mutation method on four routes. The system has been implemented in software so that it can provide recommendations on the number of ticket passes distributed in the economic subclass.

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