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A hybrid multi-objective PSO algorithm with local search strategy for VLSI partitioning

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Abstract

Very large scale integration (VLSI) circuit partitioning is an important problem in design automation of VLSI chips and multichip systems; it is an NP-hard combinational optimization problem. In this paper, an effective hybrid multi-objective partitioning algorithm, based on discrete particle swarm optimzation (DPSO) with local search strategy, called MDPSO-LS, is presented to solve the VLSI twoway partitioning with simultaneous cutsize and circuit delay minimization. Inspired by the physics of genetic algorithm, uniform crossover and random two-point exchange operators are designed to avoid the case of generating infeasible solutions. Furthermore, the phenotype sharing function of the objective space is applied to circuit partitioning to obtain a better approximation of a true Pareto front, and the theorem of Markov chains is used to prove global convergence. To improve the ability of local exploration, Fiduccia-Matteyses (FM) strategy is also applied to further improve the cutsize of each particle, and a local search strategy for improving circuit delay objective is also designed. Experiments on ISCAS89 benchmark circuits show that the proposed algorithm is efficient.

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Authors and Affiliations

  1. College of Mathematics and Computer Science, Fuzhou University, Fuzhou, 350116, China

    Wenzhong Guo, Genggeng Liu, Guolong Chen & Shaojun Peng

Authors
  1. Wenzhong Guo
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  2. Genggeng Liu
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  3. Guolong Chen
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  4. Shaojun Peng
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Corresponding author

Correspondence to Guolong Chen.

Additional information

Wenzhong Guo received his BS and MS in Computer Science from Fuzhou University, China in 2000 and 2003, respectively. He received his PhD in communication and information systems from Fuzhou University in 2010. He is currently a full professor with the College of Mathematics and Computer Science at Fuzhou University. His research interests include mobile computing and evolutionary computation. Currently, he is the vicedirector of the Network Computing and Intelligent Information Processing Lab, which is a key Lab of Fujian Province, China.

Genggeng Liu is a doctoral candidate in the College of Mathematics and Computer Science, Fuzhou University, China. His interests include computational intelligence and very large scale integration physical design.

Guolong Chen received his BS and MS in computational mathematics from Fuzhou University, China in 1987 and 1992, respectively. He received his PhD in computer science from Xi’an Jiaotong University, China in 2002. He is a full professor with the College of Mathematics and Computer Science at Fuzhou University. His research interests include computation intelligence, computer networks, and information security. Currently, he leads the Network Computing and Intelligent Information Processing Lab, which is a key Lab of Fujian Province, China.

Shaojun Peng is a graduate student studying at the College of Mathematics and Computer science, Fuzhou University, China. His interest is evolutionary computation.

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Guo, W., Liu, G., Chen, G. et al. A hybrid multi-objective PSO algorithm with local search strategy for VLSI partitioning. Front. Comput. Sci. 8, 203–216 (2014). https://doi.org/10.1007/s11704-014-3008-y

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