[1]汤可宗,丰建文,李 芳,等.多策略自适应粒子群优化算法[J].南京理工大学学报(自然科学版),2017,41(03):301.[doi:10.14177/j.cnki.32-1397n.2017.41.03.005]
 Tang Kezong,Feng Jianwen,Li Fang,et al.Multi-strategy adaptive particle swarm optimization algorithm[J].Journal of Nanjing University of Science and Technology,2017,41(03):301.[doi:10.14177/j.cnki.32-1397n.2017.41.03.005]
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多策略自适应粒子群优化算法()
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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:
41卷
期数:
2017年03期
页码:
301
栏目:
出版日期:
2017-06-30

文章信息/Info

Title:
Multi-strategy adaptive particle swarm optimization algorithm
文章编号:
1005-9830(2017)03-0301-06
作者:
汤可宗1丰建文1李 芳1杨静宇2
1.景德镇陶瓷大学 信息工程学院,江西 景德镇 333403; 2.南京理工大学 高维信息智能感知与系统教育部重点实验室,江苏 南京 210094
Author(s):
Tang Kezong1Feng Jianwen1Li Fang1Yang Jingyu2
1.School of Information Engineering,Jingdezhen Ceramic Institute,Jingdezhen 333403,China; 2.Key Laboratory of Intelligent Perception and Systems for High-dimensional Information ofMinistry of Education,Nanjing University of Science and Technology,Nanjing 210094,China
关键词:
粒子群优化 多样性测试 实时交替策略 精英学习策略 种群多样性
Keywords:
particle swarm optimization diversity-measurement real-time alternating strategy elitist learning strategy population diversity
分类号:
TP391.41
DOI:
10.14177/j.cnki.32-1397n.2017.41.03.005
摘要:
为了提高粒子群优化算法搜索最优解的效率,该文提出多策略自适应粒子群优化(MAPSO)算法。通过构建多样性测试方式评价种群的分布性。粒子的进化状态分别为勘探或开发状态,通过执行实时交替策略,确定粒子的进化状态。在迭代优化时,根据粒子的多样性动态地控制惯性系数。基于所构建的多样性测试方式,通过融入精英学习策略进一步改善种群多样性,以阻止种群陷入局部解。实验结果表明,与自适应性粒子群优化(APSO)、综合性学习粒子群优化(CPSO)、振荡粒子群优化(PPSO)算法相比,MAPSO算法能够持续地改善PSO跳出局部最优解的能力,其可靠性和成功率均优于其它算法,并能有效改善搜索性能和收敛速度。
Abstract:
In order to improve the efficiency of particle swarm optimization(PSO)algorithm for searching for optimal solutions,a multi-strategy adaptive particle swarm optimization(MAPSO)algorithm is proposed.A diversity-measurement strategy is developed to evaluate the population distribution.A real-time alternating strategy is performed to determine predefined evolutionary states,exploration or exploitation.During iterative optimization,the inertia weight is dynamically controlled according to the diversity of particles.An elitist learning strategy is introduced to enhance population diversity and to prevent the population from possibly falling into local optimal solutions.Experimental results show that,compared with the adaptive particle swarm optimization(APSO),comprehensive learning particle swarm optimization(CLPSO)and perturbed particle swarm optimization(PPSO),the MAPSO can substantially enhance the ability of jumping out of the local optimal solutions and significantly improve the search efficiency and convergence speed.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2016-03-18 修回日期:2016-10-10
基金项目:国家自然科学基金(61662037); 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); 江西省科技计划项目(20161BAB212042); 江西省教育厅科学技术研究项目(GJJ150927)
作者简介:汤可宗(1978-),男,博士,副教授,主要研究方向:智能信息处理,E-mail:tangkezong@126.com。
引文格式:汤可宗,丰建文,李芳,等.多策略自适应粒子群优化算法[J].南京理工大学学报,2017,41(3):301-306.
投稿网址:http://zrxuebao.njust.edu.cn
更新日期/Last Update: 2017-06-30