群活性反馈的变异自适应分数阶粒子群优化

doi:10.3969/j.issn.0253-2778.2020.07.021

中国科学技术大学学报 ›› 2020, Vol. 50 ›› Issue (7): 1026-1034.DOI: 10.3969/j.issn.0253-2778.2020.07.021

• 论著 • 上一篇

群活性反馈的变异自适应分数阶粒子群优化

苏守宝，陈秋鑫，王池社，李智

1.金陵科技学院数据科学与智慧软件江苏省重点实验室, 江苏南京 2111169; 2.江苏科技大学计算机学院, 江苏镇江 212003

收稿日期:2020-04-23 修回日期:2020-07-28 接受日期:2020-07-28 出版日期:2020-07-31 发布日期:2020-07-28
通讯作者: 王池社
作者简介:苏守宝，男，1965年生，博士/教授. 研究方向：群智能大数据挖掘等. E-mail: showbo@jit.edu.cn
基金资助:
国家自然科学基金(61375121, 41801303),金科院高层次引进人才科研项目(jit-rcyj-201505),江苏省高校省级自然科学研究重大项目(17KJA520001,18KJA520003),江苏省高校优秀科技创新团队项目(苏教科[2017]6号)资助.

Adaptive fractional order particle swarm optimization using swarm activity feedback and mutation operator

SU Shoubao, CHEN Qiuxin, WANG Chishe, LI Zhi

1. Jiangsu Key Laboratory of Data Science and Smart Software, Jinling Institute of Technology, Nanjing 211169, China; 2. School of Computer Science, Jiangsu University of Science and Technology, Zhenjiang 212003, China

Received:2020-04-23 Revised:2020-07-28 Accepted:2020-07-28 Online:2020-07-31 Published:2020-07-28

摘要/Abstract

摘要： 针对传统分数阶粒子群优化（FOPSO）在算法综合性能上依赖于分数阶次α，易陷入早熟收敛，为此提出一种基于群活性反馈的S型自适应分数阶粒子群方法（SFOPSO），即根据种群活性以及粒子个体的活跃程度自适应动态调整每个粒子的分数阶次α，使种群在搜索过程中保持较好的稳定性与多样性；同时设计了一种混合变异机制以提升种群在探索期和开发期跳出局部最优的能力. 理论分析证明了提出的算法SFOPSO的收敛性，实验选取6个不同特征的基准优化函数进行测试，结果证明了所提出SFOPSO算法的可行性和有效性，5种方法性能比较分析表明，SFOPSO具有更好的收敛精度和收敛速度.

关键词: 粒子群优化, 自适应, 变异算子, 分数阶, 群活性

Abstract: The basic particle swarm optimizer with fractional-order (FOPSO) is easy to fall into premature convergence, because its overall performance depends on the fractional order α. To solve the problem, a new adaptive fractional-order PSO algorithm, SFOPSO is proposed, by cooperating mutation operators into swarm activity feedback with S-model. During the iteration of this new algorithm, the fractional-order α of particles is adjusted adaptively according to the swarm activity with S-model and the activity value of single particles. At the same time, to enhance the ability of the swarm to escape out of local optimum during the process of exploitation or exploration, the hybrid model was designed by using mutation operators. The convergence of the proposed algorithm SFOPSO is analyzed theoretically and the experimental results show that the proposed algorithm is practicable and effective in improving convergence accuracy and convergence speed.

Key words: particle swarm optimization, adaptive, mutation operator, fractional-order, swarm activity

中图分类号:

TP18
TP303.6

苏守宝，陈秋鑫，王池社，李智. 群活性反馈的变异自适应分数阶粒子群优化[J]. 中国科学技术大学学报, 2020, 50(7): 1026-1034.

SU Shoubao, CHEN Qiuxin, WANG Chishe, LI Zhi. Adaptive fractional order particle swarm optimization using swarm activity feedback and mutation operator[J]. Journal of University of Science and Technology of China, 2020, 50(7): 1026-1034.

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群活性反馈的变异自适应分数阶粒子群优化

Adaptive fractional order particle swarm optimization using swarm activity feedback and mutation operator

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