An improved particle swarm optimizer for placement constraints

Authors:
Sheng-Ta Hsieh;Tsung-Ying Sun;Chan-Cheng Liu;Cheng-Wei Lin
Affiliations:
Department of Electrical Engineering, National Dong Hwa University, Hualien, Taiwan and Department of Communication Engineering, Oriental Institute of Technology, Taipei County, Taiwan;Department of Electrical Engineering, National Dong Hwa University, Hualien, Taiwan;Department of Electrical Engineering, National Dong Hwa University, Hualien, Taiwan;Department of Electrical Engineering, National Dong Hwa University, Hualien, Taiwan and Silicon Motion Technology Corporation, Jhubei City, Hsinchu County, Taiwan
Venue:
Journal of Artificial Evolution and Applications - Particle Swarms: The Second Decade
Year:
2008

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Abstract

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.