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中文摘要：
针对大规模混合极性Reed-Muller（Mixed Polarity Reed-Muller,MPRM）逻辑电路的延时与面积优化,提出一种基于多策略离散粒子群优化（Multi-Strategy Discrete Particle Swarm Optimization,MSDPSO）的极性搜索方法.在MSDPSO算法中,对粒子进行团队划分,每个团队既执行不同策略,又相互联系,并行完成探索与开发的双重任务.同时在进化过程中采用高斯调整来激活寻优能力较差的粒子.结合MSDPSO算法和列表极性转换技术,对大规模MPRM电路进行延时与面积极性搜索.最后对PLA格式的MCNC Benchmark电路进行算法性能测试,结果验证了MSDPSO算法的有效性.与离散粒子群优化（Discrete Particle Swarm Optimization,DPSO）算法的优化结果相比较,MSDPSO算法获取的电路延时平均缩短8.43%,面积平均节省38.36%.
英文摘要：
In order to improve the delay and area design of large-scale MPRM circuits, the multi-strategy discrete par- ticle swarm optimization（MSDPSO） is proposed. In MSDPSO, the particles were divided into several teams with different strategy, and each team cooperated with others to promote the exploration and exploitation of the particle population. Meanwhile, the Gaussian adjustment was adopted to activate the worse individuals. Combined with MSDPSO and tabular tech- nique, the best polarity of delay and area was searched for large-scale MPRM circuits. MCNC Benchmarks with PLA format are tested to verify the effectiveness of the MSDPSO, and the results show that MSDPSO has achieved an average saving of 8.46% and 38.73% on delay and area respectively in comparison with the DPSO.
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