The minimum crossing number problem is among the oldest and most fundamental problems arising in the area of automatic graph drawing. In this paper, eight population-based meta-heuristic algorithms are utilized to tackle the minimum crossing number problem for two special types of graphs, namely complete graphs and complete bipartite graphs. A 2-page book drawing representation is employed for ...

applying nonlinear models to estimation and forecasting economic models are now becoming more common, thanks to advances in computing technology. artificial neural networks (ann) models, which are nonlinear local optimizer models, have proven successful in forecasting economic variables. most ann models applied in economics use the gradient descent method as their learning algorithm. however, t...

guiding vehicles to their destination under dynamic traffic conditions is an important topic in the field of intelligent transportation systems (its). nowadays, many complex systems can be controlled by using multi agent systems. adaptation with the current condition is an important feature of the agents. in this research, formulation of dynamic guidance for vehicles has been investigated based...

in this paper a non linear integrated fuzzy multi-objective production planning model with the labor learning and machines deterioration effects is presented. the objective function consists of two quantitative objectives namely increase profits and reduces the cost of system failure and a qualitative objective namely increases the satisfaction rate of the customers. different weights for objec...

This article has been compiled to identify the best model of housing price forecasting using machine learning methods with maximum accuracy and minimum error. Five important machine learning algorithms are used to predict housing prices, including Nearest Neighbor Regression Algorithm (KNNR), Support Vector Regression Algorithm (SVR), Random Forest Regression Algorithm (RFR), Extreme Gradient B...