The Comparative Analysis of Two Neural Networks Models in the Function of the Linear Ship’s Route Costs Minimization the Comparative Analysis of Two Neural Networks Models in the Function of the Linear Ship’s Route Costs Minimization
نویسندگان
چکیده
The paper deals with a comparison between two different methods in generating sub-optimal solutions to the Hopfield-Tank TSP (traveling salesman problem) neural algorithm. Namely, the Hopfield-Tank TSP neural algorithm has been applied in the paper to the linear ship’s route, that is cycle voyage modeling, which means here finding the optimal visiting order of a given set of ports in such way as to minimize the total sailing distance and implicitly total linear ship’s traveling costs. The methods being implemented here into Hopfield-Tank TSP neural structure and computationally compared are: the brute force method and the fast insertion heuristic.
منابع مشابه
Prediction of Blasting Cost in Limestone Mines Using Gene Expression Programming Model and Artificial Neural Networks
The use of blasting cost (BC) prediction to achieve optimal fragmentation is necessary in order to control the adverse consequences of blasting such as fly rock, ground vibration, and air blast in open-pit mines. In this research work, BC is predicted through collecting 146 blasting data from six limestone mines in Iran using the artificial neural networks (ANNs), gene expression programming (G...
متن کاملA Comparative Study Concerning Linear and Nonlinear Models to Determine Sugar Content in Sugar Beet by Near Infrared Spectroscopy (NIR)
This paper reports on the use of Artificial Neural Networks (ANN) and Partial Least Squareregression (PLS) combined with NIR spectroscopy (900-1700 nm) to design calibration models for thedetermination of sugar content in sugar beet. In this study a total of 80 samples were used as the calibration set,whereas 40 samples were used for prediction. Three pre-processing methods, including Multiplic...
متن کاملDeveloping a Radial Basis Function Neural Networks to Predict the Working Days for Tillage Operation in Crop Production
The aim of this study was to determine the probability of working days (PWD) for tillage operation using weather data with Multiple Linear Regression (MLR) and Radial Basis Function (RBF) artificial networks. In both models, seven variables were considered as input parameters, namely minimum, average and maximum temperature, relative humidity, rainfall, wind speed, and evaporation on a daily ba...
متن کاملThe relationship between Neural Networks and DEA-R (Case Study: Companies Stock Exchange)
Evaluate the performance of companies on the Stock Exchange using non-parametric methods is very important. DEA and DEA-R with the strategies for piecewise linear frontier production function and use of available data, assess the stock company. In this study, using a neural network algorithm DEA and DEA-R is suggested to classify the first companies in the stock exchange; Secondly, using the...
متن کاملEstimation of Industrial Production Costs, Using Regression Analysis, Neural Networks or Hybrid Neural - Regression Method?
Estimation (Forecasting) of industrial production costs is one of the most important factor affecting decisions in the highly competitive markets. Thus, accuracy of the estimation is highly desirable. Hibrid Regression Neural Network is an approach proposed in this paper to obtain better fitness in comparison with Regression Analysis and the Neural Network methods. Comparing the estimated resul...
متن کامل