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.

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.

The Optimization of Adaptive PID Control Algorithm Based on RBF Neural Network

2014 ◽  
Vol 998-999 ◽  
pp. 943-946
Author(s):  
Jing Liu ◽  
Guo Xin Wang
Keyword(s):  
Neural Network ◽  
Pid Controller ◽  
Rbf Neural Network ◽  
High Reliability ◽  
Simple Algorithm ◽  
Poor Performance ◽  
Learning Ability ◽  
The Neural Network ◽  
Pid Controller Tuning ◽  
Deterministic Control

As the earliest practical controller, PID controller has more than 50 years of history, and it is still the most widely used and most common industrial controllers. PID controller is simple to understand and use, without a prerequisite for an accurate model of the physical system, thus become the most popular, the most common controller. The reason why PID controller is the first developed one is that its simple algorithm, robustness and high reliability. It is widely used in process control and motion control, especially for accurate mathematical model that can be established deterministic control system. But the conventional PID controller tuning parameters are often poor performance, poor adaptability to the operating environment. The neural network has a strong nonlinear mapping ability, competence, self-learning ability of associative memory, and has a viable quantities of information processing methods and good fault tolerance.

scholarly journals Robust Hybrid Optimization Method to Reduce Investment Portfolio Risk using Fusion of Modern Portfolio Theory and Genetic Algorithm

2019 ◽  
Vol 8 (6S3) ◽  
pp. 136-148
Keyword(s):  
Genetic Algorithm ◽  
Optimization Method ◽  
Portfolio Theory ◽  
Hybrid Optimization ◽  
Investment Risk ◽  
Portfolio Risk ◽  
Algorithm Optimization ◽  
Investment Portfolio ◽  
Genetic Algorithm Optimization ◽  
Modern Portfolio Theory

Main objective of this study is to develop hybrid optimization method for reducing investment portfolio risk. The methods selected in this study are the combination of Modern Portfolio Theory (MPT) and genetic algorithm optimization approach. Three stocks from Malaysian Stock Exchange are selected in developing the investment portfolio namely Malayan Banking Berhad, Hap Seng Consolidated Berhad and Top Glove Corporation Berhad. Result indicates the modern portfolio theory can give optimal portfolio weightage with maximum return for tolerate level of investment risk. In addition, genetic algorithm enhanced the optimal searching method to find global minimum of investment risk. Result shows the minimum portfolio risk in objective function is 2.122118 with implementation genetic algorithm optimization. The optimal combination of portfolio investment is 32.24 % in asset A (Malayan Banking Berhad), 52.37 % in asset B (Hap Seng Consolidated Berhad), and 15.30 % in asset C (Top Gove Corporation Berhad). The important of this study is it will assist investor in making better decision to optimize their return for given level of investment risk. Furthermore, this hybrid method provides a better accuracy of prediction for return of investment and portfolio risk.

scholarly journals Sizing and shape optimization material use in 10 bar trusses

2021 ◽  
Vol 343 ◽  
pp. 04004
Author(s):  
Nenad Petrović ◽  
Nenad Kostić ◽  
Vesna Marjanović ◽  
Ileana Ioana Cofaru ◽  
Nenad Marjanović
Keyword(s):  
Genetic Algorithm ◽  
Shape Optimization ◽  
Cross Sections ◽  
Optimization Model ◽  
Structural Characteristics ◽  
Optimization Method ◽  
Truss Optimization ◽  
Algorithm Optimization ◽  
Sizing Optimization ◽  
Bill Of Materials

Truss optimization has the goal of achieving savings in costs and material while maintaining structural characteristics. In this research a 10 bar truss was structurally optimized in Rhino 6 using genetic algorithm optimization method. Results from previous research where sizing optimization was limited to using only three different cross-sections were compared to a sizing and shape optimization model which uses only those three cross-sections. Significant savings in mass have been found when using this approach. An analysis was conducted of the necessary bill of materials for these solutions. This research indicates practical effects which optimization can achieve in truss design.

SYNTHESIS OF UNIFORM LINEAR ANTENNA ARRAY USING GENETIC ALGORITHM WITH COST BASED ROULETTE TO MAXIMIZE SIDELOBE LEVEL REDUCTION

2011 ◽  
Vol 08 (02) ◽  
pp. 171-179
Author(s):  
T. S. JEYALI LASEETHA ◽  
R. SUKANESH
Keyword(s):  
Genetic Algorithm ◽  
Antenna Array ◽  
Radiation Pattern ◽  
Optimization Method ◽  
Adaptive Beamforming ◽  
Linear Antenna ◽  
Algorithm Optimization ◽  
Sidelobe Level ◽  
Linear Antenna Array ◽  
Sidelobe Level Reduction

This paper discusses the deployment of Genetic Algorithm optimization method for the synthesis of antenna array radiation pattern in adaptive beamforming. The synthesis problem discussed is to find the weights of the Uniform Linear Antenna array elements that are optimum to provide the radiation pattern with maximum reduction in the sidelobe level. This technique proved its effectiveness in improving the performance of the antenna array.

GA-CDFM Based Hybrid Optimization Method for Steelmaking Scheduling and Caster Operation

2012 ◽  
Vol 424-425 ◽  
pp. 994-998 ◽  
Author(s):  
Xiao Chuan Luo ◽  
Chong Zheng Na
Keyword(s):  
Genetic Algorithm ◽  
Integer Programming ◽  
Mixed Integer Programming ◽  
Programming Model ◽  
Optimization Method ◽  
Optimal Performance ◽  
Hybrid Optimization ◽  
Mixed Integer ◽  
Algorithm Optimization ◽  
Genetic Algorithm Optimization

In steelmaking plant, the process times of machines change frequently and randomly for the reason of metallurgical principle. When those change happen, the plant scheduling and caster operation must respond to keep the optimal performance profile of plant. Therefore, the integration of plant scheduling and caster operation is a crucial task. This paper presents a mixed-integer programming model and a hybrid optimized algorithm for caster operation and plant scheduling, which combine the genetic algorithm optimization and CDFM process status verification. Data experiments illustrate the efficiency of our model and algorithm.

Optimization of Fluidized Bed Reactor of Oxidative Coupling of Methane

2012 ◽  
Vol 10 (1) ◽  
pp. 1-21 ◽  
Author(s):  
Mohammad-Hosein Eghbal-Ahmadi ◽  
Masoud Zaerpour ◽  
Mahdi Daneshpayeh ◽  
Navid Mostoufi
Keyword(s):  
Fluidized Bed ◽  
Oxidative Coupling ◽  
Operating Temperature ◽  
Fluidized Bed Reactor ◽  
Optimization Method ◽  
Oxidative Coupling Of Methane ◽  
Process Variables ◽  
Algorithm Optimization ◽  
Reactor Performance ◽  
Better Than

Optimization of process variables in the oxidative coupling of methane (OCM) over Mn/Na2WO4/SiO2 catalyst in a fluidized bed reactor was carried out. Effects of operating temperature, distribution pattern of oxygen injected to the reactor and the number of injections on the reactor performance on C2 (ethane + ethylene) yield were investigated. Process variables for one, two and three secondary oxygen injections were investigated to obtain the maximum C2 yield by genetic algorithm optimization method. The maximum C2 selectivity and yield of 47.1% and 22.87%, respectively, were achieved for three secondary oxygen injections at operating temperature of 746.05°C. The C2 yield achieved in this study is approximately 4% better than previous works reported in literature while the optimum temperature is lower.

Online minimum error entropy algorithm with unbounded sampling

2019 ◽  
Vol 17 (02) ◽  
pp. 293-322 ◽  
Author(s):  
Cheng Wang ◽  
Ting Hu
Keyword(s):  
Hilbert Spaces ◽  
Reproducing Kernel ◽  
Regression Function ◽  
Optimization Method ◽  
Low Complexity ◽  
Learning Ability ◽  
Information Theoretic ◽  
Minimum Error ◽  
Information Theoretic Learning ◽  
Kernel Hilbert Spaces

Minimum error entropy (MEE) criterion is an important optimization method in information theoretic learning (ITL) and has been widely used and studied in various practical scenarios. In this paper, we shall introduce the online MEE algorithm for dealing with big datasets, associated with reproducing kernel Hilbert spaces (RKHS) and unbounded sampling processes. Explicit convergence rate will be given under the conditions of regularity of the regression function and polynomially decaying step sizes. Besides its low complexity, we will also show that the learning ability of online MEE is superior to the previous work in the literature. Our main techniques depend on integral operators on RKHS and probability inequalities for random variables with values in a Hilbert space.

Heterogeneous Electrode Designs for Planar SOFC Optimizations

2011 ◽  
Author(s):  
Junxiang Shi ◽  
Xingjian Xue
Keyword(s):  
Solid Oxide Fuel Cells ◽  
High Performance ◽  
Performance Enhancement ◽  
High Current Density ◽  
Porous Electrode ◽  
Model Development ◽  
Optimization Method ◽  
Operating Conditions ◽  
Algorithm Optimization ◽  
Pressure Losses

Suitable porous electrode design may play a significant role in the performance enhancement of solid oxide fuel cells (SOFCs). In this paper, a genetic algorithm optimization method is employed to design electrodes based on a 2-D planar SOFC model development. The objective is to find out suitable porosity and particle size distributions for both anode and cathode electrodes so that the cell performance can be maximized. The results indicate that the optimized heterogeneous electrode may better improve SOFC performance than the homogeneous count-part, particularly under relatively high current density conditions. The optimization results are dependent on the operating conditions. The effects of pressure losses along the anode/cathode channels and inlet fuel compositions are investigated. The proposed approach provides a systematical method for electrode microstructure designs of high performance SOFCs.

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