适应度二次选择的QPSO和SA协同搜索大规模离散优化算法

张兆娟; 王万良; 唐继军

doi:10.11959/j.issn.1000-436x.2020173

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适应度二次选择的QPSO和SA协同搜索大规模离散优化算法

学术论文 | 更新时间：2024-06-05

- 适应度二次选择的QPSO和SA协同搜索大规模离散优化算法
- Second fitness selection QPSO and SA cooperative search for large-scale discrete optimization algorithm
- 通信学报 2020年41卷第8期页码：22-31
- 作者机构：
  
  1. 浙江工业大学计算机科学与技术学院，浙江杭州 310023
  2. 天津大学智能与计算学部，天津 300050
- 作者简介：
  
  [ "张兆娟（1990- ），女，江西九江人，浙江工业大学博士生，主要研究方向为大数据、分布式优化、深度学习等" ]
  [ "王万良（1957- ），男，江苏高邮人，博士，浙江工业大学教授、博士生导师，主要研究方向为人工智能、大数据分析、优化调度、计算机智能自动化等" ]
  [ "唐继军（1971- ），男，湖南常德人，博士，天津大学特聘教授、博士生导师，主要研究方向为深度学习、生物信息、高性能计算等" ]
- 基金信息：
  
  国家自然科学基金资助项目(61873240)
- DOI：10.11959/j.issn.1000-436x.2020173
  中图分类号： TP301
- 网络出版日期：2020-08，
  
  纸质出版日期：2020-08-25
- 稿件说明：
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张兆娟, 王万良, 唐继军. 适应度二次选择的QPSO和SA协同搜索大规模离散优化算法[J]. 通信学报, 2020,41(8):22-31.

Zhaojuan ZHANG, Wanliang WANG, Jijun TANG. Second fitness selection QPSO and SA cooperative search for large-scale discrete optimization algorithm[J]. Journal on communications, 2020, 41(8): 22-31.
张兆娟, 王万良, 唐继军. 适应度二次选择的QPSO和SA协同搜索大规模离散优化算法[J]. 通信学报, 2020,41(8):22-31. DOI： 10.11959/j.issn.1000-436x.2020173.

Zhaojuan ZHANG, Wanliang WANG, Jijun TANG. Second fitness selection QPSO and SA cooperative search for large-scale discrete optimization algorithm[J]. Journal on communications, 2020, 41(8): 22-31. DOI： 10.11959/j.issn.1000-436x.2020173.

摘要

针对大规模离散工程优化问题，提出一种改进的离散量子粒子群优化算法（IDQPSO-SA）。首先，提出一种适应度的二次选择更新平均最优位置策略，使QPSO能够适用离散空间的优化问题。其次，引入二次切割与连接（DCJ）排序策略加速搜索进程。最后，在QPSO并行搜索基础上，引进模拟退火（SA）的概率突跳性，协同进行全局搜索。在大规模、高维离散工程优化问题上进行了测试，并同已有算法进行比较，结果表明，IDQPSO-SA进一步提高了面向大规模离散优化问题时的搜索效率，并有效提升了算法的性能。

Abstract

To address the large-scale discrete optimization problem

a cooperative optimization algorithm called IDQPSO-SA was proposed.First

a strategy by applying two selections on the averaging fitness values to update the mean best position was presented

which could overcome the deficiency that QPSO was not applicable for discrete problems.Second

the double cut joining (DCJ) sorting strategy was incorporated into IDQPSO-SA

since the DCJ sorting strategy could considerably reduce the search space.Finally

the probability jumping ability of simulated annealing (SA) was combined with the parallel search of QPSO

and the global search was carried out collaboratively.By comparing with existing algorithms

the experimental results show that IDQPSO-SA further improves the search efficiency and has a comparable performance when faced with large-scale discrete optimization problems.

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references

王凌 , 沈婧楠 , 王圣尧 , 等 . 协同进化算法研究进展 [J ] . 控制与决策 , 2015 , 30 ( 2 ): 193 - 202 .

WANG L , SHEN J N , WANG S Y , et al . Advances in co-evolutionary algorithms [J ] . Control and Decision , 2015 , 30 ( 2 ): 193 - 202 .

王万良 , 张兆娟 , 高楠 , 等 . 基于人工智能技术的大数据分析方法研究进展 [J ] . 计算机集成制造系统 , 2019 , 25 ( 3 ): 529 - 547 .

WANG W L , ZHANG Z J , GAO N , et al . Research progress of big data analytics methods based on artificial intelligence technology [J ] . Computer Integrated Manufacturing Systems , 2019 , 25 ( 3 ): 529 - 547 .

GHEYAS I A , SMITH L S . Feature subset selection in large dimensionality domains [J ] . Pattern Recognition , 2010 , 43 ( 1 ): 5 - 13 .

张震 , 魏鹏 , 李玉峰 , 等 . 改进粒子群联合禁忌搜索的特征选择算法 [J ] . 通信学报 , 2018 , 39 ( 12 ): 60 - 68 .

ZHANG Z , WEI P , LI Y F , et al . Feature selection algorithm based on improved particle swarm joint taboo search [J ] . Journal on Communi-cations , 2018 , 39 ( 12 ): 60 - 68 .

王晟 , 王雪 , 毕道伟 . 无线传感器网络遗传—禁忌搜索移动代理测量调度方法 [J ] . 通信学报 , 2008 , 29 ( 11 ): 194 - 199 .

WANG S , WANG X , BI D W . Genetic algorithm-tabu search for mo-bile agents measurement scheduling in wireless sensor networks [J ] . Journal on Communications , 2008 , 29 ( 11 ): 194 - 199 .

叶苗 , 王宇平 , 代才 , 等 . 无线传感器网络中新的最小暴露路径问题及其求解算法 [J ] . 通信学报 , 2016 , 37 ( 1 ): 49 - 60 .

YE M , WANG Y P , DAI C , et al . New minimum exposure path prob-lem and its solving algorithm in wireless sensor networks [J ] . Journal on Communications , 2016 , 37 ( 1 ): 49 - 60 .

GHEYAS I A , SMITH L S . Feature subset selection in large dimensionality domains [J ] . Pattern Recognition , 2010 , 43 ( 1 ): 5 - 13 .

SUN J , FENG B , XU W . Particle swarm optimization with particles having quantum behavior [C ] // Proceedings of the 2004 Congress on Evolutionary Computation . Piscataway:IEEE Press , 2004 : 325 - 331 .

LI L , JIAO L , ZHAO J , et al . Quantum-behaved discrete multi-objective particle swarm optimization for complex network clustering [J ] . Pattern Recognition , 2017 , 63 : 1 - 14 .

LUKEMIRE J , MANDAL A , WONG W K . d-QPSO:a quantum-behaved particle swarm technique for finding d-optimal designs with discrete and continuous factors and a binary response [J ] . Technometrics , 2019 , 61 ( 1 ): 77 - 87 .

KIRKPATRICK S . Optimization by simulated annealing:quantitative studies [J ] . Journal of Statistical Physics , 1984 , 34 ( 5-6 ): 975 - 986 .

LU R , ZHAO X , LI J , et al . Genomic characterisation and epidemiology of 2019 novel coronavirus:implications for virus origins and receptor binding [J ] . The Lancet , 2020 , 395 ( 10224 ): 565 - 574 .

WU A , PENG Y , HUANG B , et al . Genome composition and divergence of the novel coronavirus(2019-nCoV) originating in China [J ] . Cell Host ＆ Microbe , 2020 , 27 ( 3 ): 325 - 328 .

WANG S W , BITBOL A F , WINGREEN N S . Revealing evolutionary constraints on proteins through sequence analysis [J ] . PLoS Computational Biology , 2019 , 15 ( 4 ):e1007010.

WANG Y K , BASHASHATI A , ANGLESIO M S , et al . Genomic consequences of aberrant DNA repair mechanisms stratify ovarian cancer histotypes [J ] . Nature Genetics , 2017 , 49 ( 6 ):856.

TOOSI H , MOEINI A , HAJIRASOULIHA I . BAMSE:Bayesian model selection for tumor phylogeny inference among multiple samples [J ] . BMC bioinformatics , 2019 , 20 ( 11 ):282.

XU A W . A fast and exact algorithm for the median of three problem:a graph decomposition approach [J ] . Journal of Computational Biology , 2009 , 16 ( 10 ): 1369 - 1381 .

FEIJÃO P . Reconstruction of ancestral gene orders using intermediate genomes [J ] . BMC bioinformatics , 2015 , 16 ( 14 ):S3.

王万良 . 人工智能及其应用(第 4 版) [M ] . 北京 : 高等教育出版社 , 2020 .

WANG W L . Artificial intelligence:principles and applications [M ] . 4rd Ed,Beijing : Higher Education Press , 2020 .

GAO N , YANG N , TANG J . Ancestral genome inference using a genetic algorithm approach [J ] . PLoS One , 2013 , 8 ( 5 ):e62156.

GAO N , ZHANG Y , FENG B , et al . A cooperative co-evolutionary genetic algorithm for tree scoring and ancestral genome inference [J ] . IEEE/ACM Transactions on Computational Biology and Bioinformatics , 2015 , 12 ( 6 ): 1248 - 1254 .

XIA R , LIN Y , ZHOU J , et al . A median solver and phylogenetic inference based on double-cut-and-join sorting [J ] . Journal of Computational Biology , 2018 , 25 ( 3 ): 302 - 312 .

YANCOPOULOS S , ATTIE O , FRIEDBERG R . Efficient sorting of genomic permutations by translocation,inversion and block interchange [J ] . Bioinformatics , 2005 , 21 ( 16 ): 3340 - 3346 .

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