Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2013, Article ID 675381, 8 pages
http://dx.doi.org/10.1155/2013/675381
Research Article

Load Distribution of Evolutionary Algorithm for Complex-Process Optimization Based on Differential Evolutionary Strategy in Hot Rolling Process

1Key Laboratory of Advanced Control of Iron and Steel Process, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China
2Institute for Automatic Control and Complex Systems, University of Duisburg-Essen, Duisburg 47057, Germany
3Beijing Power Electronics and Motor Drives Engineering Research Center, College of Mechanical and Electrical Engineering, North China University of Technology, Beijing 100144, China

Received 5 September 2013; Revised 21 October 2013; Accepted 31 October 2013

Academic Editor: Xiao He

Copyright © 2013 Xu Yang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Based on the hot rolling process, a load distribution optimization model is established, which includes rolling force model, thickness distribution model, and temperature model. The rolling force ratio distribution and good strip shape are integrated as two indicators of objective function in the optimization model. Then, the evolutionary algorithm for complex-process optimization (EACOP) is introduced in the following optimization algorithm. Due to its flexible framework structure on search mechanism, the EACOP is improved within differential evolutionary strategy, for better coverage speed and search efficiency. At last, the experimental and simulation result shows that evolutionary algorithm for complex-process optimization based on differential evolutionary strategy (DEACOP) is the organism including local search and global search. The comparison with experience distribution and EACOP shows that DEACOP is able to use fewer adjustable parameters and more efficient population differential strategy during solution searching; meanwhile it still can get feasible mathematical solution for actual load distribution problems in hot rolling process.