Showing 2 results for M. Eftekhari
M. Eftekhari, B. Daei, and S. D. Katebi,
Volume 25, Issue 1 (7-2006)
Abstract
A novel version of Ant Colony Optimization (ACO) algorithms for solving continuous space problems is presented in this paper. The basic structure and concepts of the originally reported ACO are preserved and adaptation of the algorithm to the case of continuous space is implemented within the general framework. The stigmergic communication is simulated through considering certain direction vectors which are memorized. These vectors are normalized gradient vectors that are calculated using the values of the evaluation function and the corresponding values of object variables.
The proposed Gradient-based Continuous Ant Colony Optimization (GCACO) method is applied to several benchmark problems
and the results are compared and contrasted with other population-based algorithms such as Evolutionary Strategies (ES), Evolutionary Programming (EP), and Genetic Algorithms (GA). The results obtained from GCACO compare satisfactorily with those of other algorithms and in some cases are superior in terms of accuracy and computational demand.
Gh.r. Aghaei , M.r. Izadpanah, M. Eftekhari ,
Volume 32, Issue 2 (Dec 2013)
Abstract
Mechanical alloying technique is used for production of nanostructured soft magnetic alloys. In this work the back propagation (BP) artificial neural adopted to model the effect of various mechanical alloying parameters i.e. milling time and chemical composition, on the properties of Fe-Ni powders. Lattice parameter, grain size, lattice strain, coersivity and saturation intrinsic flux density are considered as the output of five BP neural networks. The results obtained show the efficiency of designed networks for the prediction of the properties of Fe-Ni powders.
