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基于P系统的粒子群优化算法
  • ISSN号：1001-3695
  • 期刊名称：计算机应用研究
  • 时间：2013.8.15
  • 页码：2269-2272
  • 分类：TP301.6[自动化与计算机技术—计算机系统结构;自动化与计算机技术—计算机科学与技术]
  • 作者机构：[1]山东师范大学管理科学与工程学院,济南250014
  • 相关基金：国家自然科学基金资助项目（61170038）; 山东省自然科学基金资助项目（ZR2011FM001）; 国家教育部人文社会科学研究项目（12YJA630152）; 山东省社会科学基金资助项目（11CGLJ22）; 山东省高等学校科技计划资助项目（J12LN22）; 山东省高等学校科技计划项目（J12LN65）
  • 相关项目：基于DNA计算和离散Morse方法的聚类分析研究
作者： 杜强|向来生|刘希玉|
关键词： 膜计算, 粒子群优化, 全局搜索, 局部寻优, membrane computing, particle swarm optimization（PSO）, global search, partial optimization
中文摘要：

为了克服算法早熟收敛问题并提高算法精度,引入了膜计算理论。将PSO算法与P系统相结合,提出了一种基于P系统的粒子群优化算法（P-PSO）,有效地平衡粒子群的全局搜索和局部寻优。采用常用的三个测试函数对新算法进行了实验,结果表明,提出的P-PSO算法有效地解决了算法早熟问题,提高了算法的收敛精度。由此可见,P-PSO算法能够有效改进原有PSO算法的性能。

英文摘要：

In order to overcome the premature convergence and improve the precision of the algorithm, this paper introduced the membrane computing theory. It put forward the P system based particle swarm optimization （P-PSO） to keep the balance of global search and partial optimization. The simulation results on three benchmark functions show that the P-PSO algorithm is effective to solve the problem of premature convergence, and has high accuracy. Obviously, the P-PSO algorithm can effective- ly improve the performance of the original PSO algorithm.

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期刊信息
  • 《计算机应用研究》
  • 北大核心期刊（2011版）
  • 主管单位:四川省科学技术厅
  • 主办单位:四川省计算机研究院
  • 主编：刘营
  • 地址：成都市成科西路3号
  • 邮编：610041
  • 邮箱：arocmag@163.com
  • 电话：028-85210177 85249567
  • 国际标准刊号：ISSN：1001-3695
  • 国内统一刊号：ISSN：51-1196/TP
  • 邮发代号:62-68
  • 获奖情况:
  • 第二届国家期刊奖百种重点科技期刊,国内计算技术类重点核心期刊,国内外著名数据库收录期刊
  • 国内外数据库收录:
  • 俄罗斯文摘杂志,波兰哥白尼索引,英国科学文摘数据库,日本日本科学技术振兴机构数据库,中国中国科技核心期刊,中国北大核心期刊（2004版）,中国北大核心期刊（2008版）,中国北大核心期刊（2011版）,中国北大核心期刊（2014版）,中国北大核心期刊（2000版）
  • 被引量:60049