布图规划在超大规模集成电路（VLSI）物理设计过程中具有重要作用，它是一个多目标组合优化问题且被证明是一个NP问题。为了有效解决布图规划问题，本文提出一个多目标粒子群优化(PSO)算法。该算法采用序列对表示法对粒子进行编码，根据遗传算法交叉算子的思想对粒子更新公式进行了修改；引入Pareto最优解的概念和精英保留策略，并设计了一个基于表现型共享的适应值函数以维护种群的多样性。仿真实验通过对MCNC标准问题的测试表明了本文算法是可行且有效的。

Floorplanning plays an important role in the physical design of very large scale integrated circuits(VLSI). It is a multiobjective combinatorial optimization and has been proved to be a NPhard problem. To solve this problem,a multiobjective particle swarm optimization (PSO) is proposed. The algorithm adopts sequence pair (SP) representation,thus the particle update formula is modified  by the principle of crossover operator in GA. The concept of ParetoOptimal Solution and elitism preserving strategy are imported. Moreover,a fitness function with phenotype sharing is designed to obtain a more uniformly distributed Pareto front. Experiments on the MCNC benchmarks show the proposed algorithm is feasible and effective.