[1]汤可宗,李慧颖,李 娟,等.一种求解复杂优化问题的改进粒子群优化算法[J].南京理工大学学报(自然科学版),2015,39(04):386.
 Tang Kezong,Li Huiying,Li Juan,et al.Improved particle swarm optimization algorithm for solving complex optimization problems[J].Journal of Nanjing University of Science and Technology,2015,39(04):386.
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一种求解复杂优化问题的改进粒子群优化算法
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《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

卷:
39卷
期数:
2015年04期
页码:
386
栏目:
出版日期:
2015-08-31

文章信息/Info

Title:
Improved particle swarm optimization algorithm for solving complex optimization problems
作者:
汤可宗13李慧颖2李 娟2罗立民1
1.东南大学 计算机科学与工程学院,江苏 南京 210018; 2.景德镇陶瓷学院 信息工程学院,江西 景德镇 333403; 3.南京理工大学 高维信息智能感知与系统教育重点实验室,江苏 南京 210094
Author(s):
Tang Kezong13Li Huiying2Li Juan2Luo Limin1
1.School of Computer Science and Engineering,Southeast University,Nanjing 210018,China; 2.School of Information Engineering,Jingdezhen Ceramic Institute,Jingdezhen 333403,China; 3.Loboratory of Intelligent Perception and Systems for High-dimensional Information of Ministry Education,NUST,Nanjing 210094,China
关键词:
粒子群优化 优化策略 优化问题 粒子搜索 认识系数 社会系数
Keywords:
particle swarm optimization optimization strategies optimization problems particles searching cognitive coefficients social coefficients
分类号:
TP391.41
摘要:
为了提高粒子群优化算法中粒子搜索最优解的效率,该文在标准粒子群优化算法的基础上,提出一种改进的粒子群优化算法。该方法通过对粒子飞行轨迹的分析,对种群中每个粒子构建了评价粒子性能差异的等级标准,并对认识系数和社会系数设计了对应的动态变化系数模型。通过引入迁徙策略,使迁徙行为随机生成的新粒子更有可能接近全局最优解,更加有利于群体搜索跳出局部最优解和寻找全局最优解。实验结果表明,与其他比较算法相比,该文提出的改进粒子群优化算法具有寻优能力强和搜索精度高等优点,测试准测上的实验数据验证了改进算法的有效性和可行性。
Abstract:
To improve the efficiency of the particle swarm optimization algorithm for particles searching optimal solutions,an improved particle swarm optimization(IPSO)algorithm is proposed based on the standard PSO.Each particle has a corresponding grading standard by trajectory analysis of flight path,and two dynamic models of coefficients are designed for the cognitive coefficient and social one,respectively.In addition,through the introduction of the migration strategy,the newly obtained particles are more likely closer to the global optimal solution to a certain extent,and it is easy to jump out of the local optimal solution to search for the optimal solution.Simulation results show the IPSO algorithm has powerful optimizing ability and higher search veracity.The experimental data on the test criterion verify the effectiveness and the feasibility of the improved algorithm.

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备注/Memo

备注/Memo:
收稿日期:2014-09-25 修回日期:2014-12-22
基金项目:国家自然科学基金(61202313,51362016); 江苏省博士后科研资助计划(1402019C); 江西省自然科学基金(2012BAB201044); 高维信息智能感知与系统教育部重点实验室开放课题资助课题(JYB201507)
作者简介:汤可宗(1978-),男,博士后,主要研究方向:模式识别与图像处理,E-mail:tangkezong@126.com。
引文格式:汤可宗,李慧颖,李娟,等.一种求解复杂优化问题的改进粒子群优化算法[J].南京理工大学学报,2015,39(4):386-391.
投稿网址:http://zrxuebao.njust.edu.cn
更新日期/Last Update: 2015-08-31