Table of Contents
Journal of Artificial Evolution and Applications
Volume 2008, Article ID 514879, 10 pages
Research Article

An Improved Particle Swarm Optimizer for Placement Constraints

1Department of Electrical Engineering, National Dong Hwa University, Hualien 97401, Taiwan
2Department of Communication Engineering, Oriental Institute of Technology, Taipei County 22061, Taiwan
3Silicon Motion Technology Corporation, Jhubei City, Hsinchu County 302, Taiwan

Received 20 July 2007; Revised 28 November 2007; Accepted 14 January 2008

Academic Editor: Alex Freitas

Copyright © 2008 Sheng-Ta Hsieh 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.


This paper presents a macrocell placement constraint and overlap removal methodology using an improved particle swarm optimization (PSO). Several techniques have been proposed to improve PSO, such as methods to prevent the floorplan from falling into the local minimum and to assist in finding the global minimum. The proposed method can deal with various kinds of placement constraints and can process them simultaneously. Experiments employing MCNC and GSRC benchmarks show the difference in the efficiency and robustness of proposed method in the exploration for more optimal solutions through restricted placement and overlap removal compared with other methods. The proposed approach exhibits rapid convergence and leads to more optimal solutions than other related approaches; furthermore, it displays efficient packing with all the constraints satisfied.