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山东大学学报 (工学版) ›› 2024, Vol. 54 ›› Issue (6): 147-155.doi: 10.6040/j.issn.1672-3961.0.2023.210

• 电气工程 • 上一篇    

基于AMSChOA的MPRM电路面积优化

张梦雨1,2,何振学1,2*,赵晓君1,2,王浩然3,肖利民4,王翔5   

  1. 1.河北农业大学智能农业装备研究院, 河北 保定 071001;2.河北农业大学河北省农业大数据重点实验室, 河北 保定 071001;3.河北农业大学林学院, 河北 保定 071001;4.北京航空航天大学计算机学院, 北京 100191;5.北京航空航天大学电子信息工程学院, 北京 100191
  • 发布日期:2024-12-26
  • 作者简介:张梦雨(2000— ),女,河南郑州人,硕士研究生,主要研究方向为RM电路智能优化. E-mail: 20222060110@pgs.hebau.edu.cn. *通信作者简介:何振学(1987— ),男,山东泰安人,副教授,博士生导师,博士,主要研究方向为智能优化算法、最优化理论、电子设计自动化. E-mail:hezhenxue@buaa.edu.cn
  • 基金资助:
    国家自然科学基金资助项目(62102130);中央引导地方科技发展资金资助项目(226Z0201G);河北省自然科学基金资助项目(F2020204003);河北省青年拔尖人才计划资助项目(BJ2019008);河北省高等学校科学技术研究资助项目(QN2022138);河北省省属高等学校基本科研业务费研究资助项目(KY2022073)

Area optimization for MPRM circuits based on AMSChOA

ZHANG Mengyu1,2, HE Zhenxue1,2*, ZHAO Xiaojun1,2, WANG Haoran3, XIAO Limin4, WANG Xiang5   

  1. 1. Intelligent Agricultural Equipment Research Institute, Hebei Agricultural University, Baoding 071001, Hebei, China;
    2. Key Laboratory of Agricultural Big Data of Hebei Province, Hebei Agricultural University, Baoding 071001, Hebei, China;
    3. College of Forestry, Hebei Agricultural University, Baoding 071001, Hebei, China;
    4. School of Computer Science and Engineering, Beihang University, Beijing 100191, China;
    5. School of Electronic Information Engineering, Beihang University, Beijing 100191, China
  • Published:2024-12-26

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摘要： 为解决现有基于同或/或(XNOR/OR)的混合极性Reed-Muller(mixed polarity Reed-Muller, MPRM)电路面积优化方法中存在的收敛速度较慢、不容易跳出局部最优等问题,提出一种基于自适应多策略选择黑猩猩优化算法(adaptive multi-strategy selection chimp optimization algorithm, AMSChOA)的MPRM电路面积优化方法。AMSChOA使用柯西变异、螺旋搜索、随机搜索和翻筋斗策略在4个最优黑猩猩附近进行搜索,扩大算法的搜索范围。针对其他黑猩猩个体加入动态学习因子策略,动态学习4个最优黑猩猩位置,加快算法跳出局部最优。利用提出的AMSChOA对基于XNOR/OR的MPRM电路进行面积优化,搜索电路面积最小时对应的MPRM电路。基于北卡罗来纳微电子中心(Microelectronics Center of North Carolina, MCNC)基准测试电路的试验结果表明,本研究提出的方法有效,与基于传统黑猩猩优化算法、粒子群算法、改进粒子群算法的MPRM电路面积优化方法相比,最高面积优化率为68.09%,平均优化率为41.24%。

关键词: MPRM面积优化, 自适应多策略选择黑猩猩优化算法, 混合极性Reed-Muller, 动态学习因子, 组合优化问题

中图分类号: 

  • V443
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