scholarly journals Optimasi Pembagian Kendaraan Penumpang pada Kapal Roro menggunakan Algoritma Genetika

2020 ◽  
Vol 9 (1) ◽  
pp. 38-44
Author(s):  
Suryaman ◽  
Eka Suswaini ◽  
Muhamad Radzi Rathomi
Keyword(s):  
Genetic Algorithm ◽  
Vehicle Load ◽  
Fitness Value

Increasing the crossing of vehicles from Tanjung Uban to Telaga Punggur or vice versa, it is necessary to optimize the selection vehicles partition and determine which vehicles will take precedence in order to achieve accuracy in terms of optimal vehicle load, therefore it takes an algorithm that can produce vehicles based on vahicles placement. This research will try to use genetic algorithm in optimizing Roro vehicles partition. The result of the experiment shows the result in Test then the best fitness result is in the 4th parameter with the number of chromosomes = 5, pc = 0.8, pm = 0.01, 0.05 and is in the 50th generation and the resulting fitness value is 0.5.

Solving Vibration Control Problems Using Reduced Order Models for Flexible Structures: A Genetic Algorithm Approach

1997 ◽  
Author(s):  
Sourav Kundu ◽  
Kentaro Kamagata ◽  
Shigeru Sugino ◽  
Takeshi Minowa ◽  
Kazuto Seto
Keyword(s):  
Genetic Algorithm ◽  
Vibration Control ◽  
Controller Design ◽  
Fitness Function ◽  
Flexible Structures ◽  
Design Solution ◽  
Reduced Order Models ◽  
Reduced Order ◽  
Fitness Value ◽  
Genetic Algorithm Approach

Abstract A Genetic Algorithm (GA) based approach for solution of optimal control design of flexible structures is presented in this paper. The method for modeling flexible structures with distributed parameters as reduced-order models with lumped parameters, which has been developed previously, is employed. Due to some restrictions on controller design it is necessary to make a reduced-order model of the structure. Once the model is established the design of flexible structures is considered as a feedback search procedure where a new solution is assigned some fitness value for the GA and the algorithm iterates till some satisfactory design solution is achieved. We propose a pole assignment method to determine the evaluation (fitness) function to be used by the GA to find optimal damping ratios in passive elements. This paper demonstrates the first results of a genetic algorithm approach to solution of the vibration control problem for practical control applications to flexible tower-like structures.

Towards Academic Successor Selection Modelling with Genetic Algorithm in Multi-Criteria Problems

2018 ◽  
Vol 7 (4.33) ◽  
pp. 130
Author(s):  
Atiqa Zukreena Zakuan ◽  
Shuzlina Abdul-Rahman ◽  
Hamidah Jantan ◽  
. .
Keyword(s):  
Genetic Algorithm ◽  
Decision Making ◽  
Succession Planning ◽  
Fitness Function ◽  
Academic Staff ◽  
Fitness Value ◽  
Selection Decision ◽  
The University ◽  
Hierarchy Process

Succession planning is a subset of talent management that deals with multi-criteria and uncertainties which are quite complicated, ambiguous, fuzzy and troublesome. Besides that, the successor selection involves the process of searching the best candidate for a successor for an optimal selection decision. In an academic scenario, the quality of academic staff contributes to achieving goals and improving the performance of the university at the international level. The process of selecting appropriate academic staff requires good criteria in decision-making. The best candidate's position and criteria for the selection of academic staff is the responsibility of the Human Resource Management (HRM) to select the most suitable candidate for the required position. The various criteria that are involved in selecting academic staff includes research publication, teaching skills, personality, reputation and financial performance. Previously, most studies on multi-criteria decision-making adopt Fuzzy Analytical Hierarchy Process (FAHP). However, this method is more complex because it involved many steps and formula and may not produce the optimum results. Therefore, Genetic Algorithm (GA) is proposed in this research to address this problem in which a fitness function for the successor selection is based on the highest fitness value of each chromosome.    

scholarly journals Short-Term Traffic Flow Forecasting Model Based on GA-TCN

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Rongji Zhang ◽  
Feng Sun ◽  
Ziwen Song ◽  
Xiaolin Wang ◽  
Yingcui Du ◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Convolutional Neural Network ◽  
Traffic Flow ◽  
Absolute Error ◽  
Forecasting Model ◽  
Average Absolute Error ◽  
Short Term ◽  
Traffic Flow Forecasting ◽  
Fitness Value

Traffic flow forecasting is the key to an intelligent transportation system (ITS). Currently, the short-term traffic flow forecasting methods based on deep learning need to be further improved in terms of accuracy and computational efficiency. Therefore, a short-term traffic flow forecasting model GA-TCN based on genetic algorithm (GA) optimized time convolutional neural network (TCN) is proposed in this paper. The prediction error was considered as the fitness value and the genetic algorithm was used to optimize the filters, kernel size, batch size, and dilations hyperparameters of the temporal convolutional neural network to determine the optimal fitness prediction model. Finally, the model was tested using the public dataset PEMS. The results showed that the average absolute error of the proposed GA-TCN decreased by 34.09%, 22.42%, and 26.33% compared with LSTM, GRU, and TCN in working days, while the average absolute error of the GA-TCN decreased by 24.42%, 2.33%, and 3.92% in weekend days, respectively. The results indicate that the model proposed in this paper has a better adaptability and higher prediction accuracy in short-term traffic flow forecasting compared with the existing models. The proposed model can provide important support for the formulation of a dynamic traffic control scheme.

scholarly journals GA-Based Early Warning Method for Rock Burst with Microseismic and Acoustic Emission in Steeply Inclined Coal Seam

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Yarong Xue ◽  
Dazhao Song ◽  
Zhenlei Li ◽  
Jianqiang Chen ◽  
Xueqiu He ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Rock Burst ◽  
Coal Mine ◽  
Early Warning ◽  
Characteristic Parameter ◽  
Control Measures ◽  
Risk Level ◽  
Parameter System ◽  
Fitness Value ◽  
Rock Burst Risk

Aiming at problem of low efficacy of early warning of rock burst in coal mine, a multisystem and multiparameter integrated early warning method based on genetic algorithm (GA) is proposed. In this method, firstly, the temporal-spatial-intensity information of energy incubation process of rock burst is deeply mined, and the multidimensional precursory characteristic parameter system of rock burst is constructed. Secondly, the genetic algorithm is used to train the historical monitoring data to obtain the optimal critical value and fitness value of each precursory characteristic parameter, and then the early warning index WC of each monitoring system is calculated. Finally, the integrated rock burst early warning index IC is obtained by synthesizing the early warning index WC of each system. The value of IC corresponds to the specific rock burst risk level of the mine. This method is applied to Wudong coal mine in Xinjiang, China. Based on the actual situation of the mine, a multidimensional precursory characteristic parameter system of rock burst is constructed, which includes energy deviation (DE), frequency ratio (Fr), frequency deviation (DF), degree of dispersion (DS), and total high value of energy deviation (DH). After analyzing the rock burst danger status and risk level in the monitoring area, the early warning capability of this method is found to reach 0.896. Combining with the specific prevention and control measures corresponding to different rock burst risk levels, it can provide effective guidance for the field work.

A Natural Evolution Based Numerical Optimisation Framework to Develop and Enhance Airfoil-Slat Arrangement

2019 ◽  
Author(s):  
Sushrut Kumar ◽  
Priyam Gupta ◽  
Raj Kumar Singh
Keyword(s):  
Genetic Algorithm ◽  
Fitness Function ◽  
Fuel Efficiency ◽  
Optimal Solution ◽  
Leading Edge ◽  
Initial Population ◽  
Invasive Weed ◽  
Fitness Value ◽  
Leading Edge Slats ◽  
Dynamics Simulations

Abstract Leading Edge Slats are popularly being put into practice due to their capability to provide a significant increase in the lift generated by the wing airfoil and decrease in the stall. Consequently, their optimum design is critical for increased fuel efficiency and minimized environmental impact. This paper attempts to develop and optimize the Leading-Edge Slat geometry and its orientation with respect to airfoil using Genetic Algorithm. The class of Genetic Algorithm implemented was Invasive Weed Optimization as it showed significant potential in converging design to an optimal solution. For the study, Clark Y was taken as test airfoil. Slats being aerodynamic devices require smooth contoured surfaces without any sharp deformities and accordingly Bézier airfoil parameterization method was used. The design process was initiated by producing an initial population of various profiles (chromosomes). These chromosomes are composed of genes which define and control the shape and orientation of the slat. Control points, Airfoil-Slat offset and relative chord angle were taken as genes for the framework and different profiles were acquired by randomly modifying the genes within a decided design space. To compare individual chromosomes and to evaluate their feasibility, the fitness function was determined using Computational Fluid Dynamics simulations conducted on OpenFOAM. The lift force at a constant angle of attack (AOA) was taken as fitness value. It was assigned to each chromosome and the process was then repeated in a loop for different profiles and the fittest wing slat arrangement was obtained which had an increase in CL by 78% and the stall angle improved to 22°. The framework was found capable of optimizing multi-element airfoil arrangements.

A GENETIC ALGORITHM BASED ON SEXUAL SELECTION FOR THE MULTIDIMENSIONAL 0/1 KNAPSACK PROBLEMS

2012 ◽  
Vol 09 ◽  
pp. 422-431 ◽  
Author(s):  
MOHAMMAD JALALI VARNAMKHASTI ◽  
LAI SOON LEE
Keyword(s):  
Genetic Algorithm ◽  
Sexual Selection ◽  
Hamming Distance ◽  
Knapsack Problems ◽  
Fitness Value ◽  
Tournament Selection ◽  
Males And Females ◽  
A New Technique ◽  
Selection Mechanisms ◽  
Active Genes

In this study, a new technique is presented for choosing mate chromosomes during sexual selection in a genetic algorithm. The population is divided into groups of males and females. During the sexual selection, the female chromosome is selected by the tournament selection while the male chromosome is selected based on the hamming distance from the selected female chromosome, fitness value or active genes. Computational experiments are conducted on the proposed technique and the results are compared with some selection mechanisms commonly used for solving multidimensional 0/1 knapsack problems published in the literature.

Improved Genetic Algorithm Based on Operation Order Matrix Encoding for Job Shop Scheduling Problem

2011 ◽  
Vol 189-193 ◽  
pp. 4212-4215
Author(s):  
Hong Zhan ◽  
Jian Jun Yang ◽  
Lu Yan Ju
Keyword(s):  
Genetic Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Improved Genetic Algorithm ◽  
Shop Scheduling ◽  
Order Matrix ◽  
Fitness Value ◽  
Matrix Encoding

This paper presents an improved genetic algorithm for the job shop scheduling problem. We designed a new encoding method based on operation order matrix, a matrix correspond to a chromosome, the value of elements is not repetitive, that means a processing order number in all operations of all jobs. Aiming at the features of the matrix encoding, we designed the crossover and mutation methods based on jobs, and the infeasible solutions are avoided. Through adjusting the computing method of fitness value, the improved genetic algorithm takes on some self adapting capability. The proposed approach is tested on some standard instances and compared with two other approaches. The computation results validate the algorithm is efficient.

Sign in / Sign up

Export Citation Format

Share Document