Abstract A hybrid genetic simulated annealing algorithm is presented for solving the problem of VLSI standard cell placement with up to millions of cells. Firstly, to make genetic algorithm be capable of handling very large scale of standard cell placement, the strategies of small size population, dynamic updating population, and crossover localization are adopted, and the global search and local search of genetic algorithm are coordinated. Then, by introducing hill climbing (HC) and simulated annealing (SA) into the framework of genetic algorithm and the internal procedure of its operators, an effective crossover operator named Net Cycle Crossover and local search algorithms for the placement problem are designed to further improve the evolutionary efficiency of the algorithm and the quality of its placement results. In the algorithm procedure, HC method and SA method focus on array placement and non-array placement respectively. The experimental results on Peko suite3, Peko suite4 and ISPD04 benchmark circuits show that the proposed algorithm can handle array and non-array placements with 10,000~1,600,000 cells and 10,000~210,000 cells respectively, and can effectively improve the quality of placement results in a reasonable running time.
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2014, Vol. 27 
