Iran University of Science and Technology

About: Iran University of Science and Technology is a education organization based out in Tehran, Iran. It is known for research contribution in the topics: Nonlinear system & Finite element method. The organization has 12917 authors who have published 24965 publications receiving 372013 citations. The organization is also known as: Governmental Technical Institute & Advanced Art College.

Predicting Longitudinal Dispersion Coefficient in Natural Streams Using M5′ Model Tree

Abstract: The longitudinal dispersion coefficient is a key parameter in determining the distribution of pollution concentration, especially in temporally time-varying source cases after full cross-sectional mixing has occurred. Several studies have been performed to present simple formulas to predict it. However, they may not always result in an accurate prediction because of the complexity of the phenomenon. In this study, a M5′ model tree was used to develop a new model for predicting the longitudinal dispersion coefficient. The main advantages of the model trees are that (1) they provide transparent formulas and offer more insight into the obtained formulas and (2) they are more convenient to develop and employ compared with other soft computing methods. To develop the model tree, extensive field data sets consisting of hydraulic and geometrical characteristics of different rivers were used. By using error measures, the performance of the model was also compared with the performance of other existing equ...

Multi-Colony Ant Algorithm for Continuous Multi-Reservoir Operation Optimization Problem

Abstract: Ant Colony Optimization (ACO) algorithms are basically developed for discrete optimization and hence their application to continuous optimization problems require the transformation of a continuous search space to a discrete one by discretization of the continuous decision variables. Thus, the allowable continuous range of decision variables is usually discretized into a discrete set of allowable values and a search is then conducted over the resulting discrete search space for the optimum solution. Due to the discretization of the search space on the decision variable, the performance of the ACO algorithms in continuous problems is poor. In this paper a special version of multi-colony algorithm is proposed which helps to generate a non-homogeneous and more or less random mesh in entire search space to minimize the possibility of loosing global optimum domain. The proposed multi-colony algorithm presents a new scheme which is quite different from those used in multi criteria and multi objective problems and parallelization schemes. The proposed algorithm can efficiently handle the combination of discrete and continuous decision variables. To investigate the performance of the proposed algorithm, the well-known multimodal, continuous, nonseparable, nonlinear, and illegal (CNNI) Fletcher–Powell function and complex 10-reservoir problem operation optimization have been considered. It is concluded that the proposed algorithm provides promising and comparable solutions with known global optimum results.