计算机科学

计算机科学 ›› 2023, Vol. 50 ›› Issue (11): 210-219.doi: 10.11896/jsjkx.221000129

• 人工智能 • 上一篇    下一篇

基于边界自适应技术的精英交互学习粒子群算法

徐杰, 周新志   

  1. 四川大学电子信息学院 成都 610000
  • 收稿日期:2022-10-16 修回日期:2023-03-30 出版日期:2023-11-15 发布日期:2023-11-06
  • 通讯作者: 周新志(xz.zhou@scu.edu.cn)
  • 作者简介:(xjhkyjs@163.com)
  • 基金资助:
    国家自然科学基金(U1933123);四川省科技成果转移转化示范项目(2022ZHCG0042)

Multi-elite Interactive Learning Based Particle Swarm Optimization Algorithm with Adaptive Bound-handling Technique

XU Jie, ZHOU Xinzhi   

  1. College of Electronic Information,Sichuan University,Chengdu 610000,China
  • Received:2022-10-16 Revised:2023-03-30 Online:2023-11-15 Published:2023-11-06
  • About author:XU Jie,born in 1996,postgraduate. His main research interests include evolutionary algorithms and intelligent information processing.ZHOU Xinzhi,born in 1966,Ph.D,professor,Ph.D supervisor.His main research interests include the intelligent computing,intelligent control and intelligent information systems.
  • Supported by:
    National Natural Science Foundation of China(U1933123) and Sichuan Science and Technology Program(2022ZHCG0042).

PDF (PC)

2246

摘要： 粒子群优化(PSO)算法依靠粒子之间的合作行为,使其在解决诸多优化问题上显示出极大的智能。然而,由于寻优机制,粒子很容易突破可行域的边界限制,若能使该行为在寻优过程中具有明确的指导意义将有助于提高算法的优化性能;更关键的是,原始粒子群优化算法中粒子的学习对象主要集中在全局最佳粒子上,这种更新机制无疑加速了种群多样性的损失,并使种群倾向于陷入局部最优。为了进一步提高求解复杂问题时的种群多样性和收敛精度,提出了一种基于边界自适应技术的精英交互学习粒子群算法(A-EIPSO)。该算法首先在原有的PSO算法中引入了新的边界处理技术,根据越界粒子的历史位置信息和越界距离自适应地赋予粒子在解空间内的分布特征;接着在多种群技术的基础上设计了一种精英学习策略来促进子群间社会信息的交换,并由精英粒子代替全局最佳粒子指导各子群内粒子的优化行为。实验结果表明,在大多数情况下,自适应处理技术保证粒子在搜索空间内实现均匀探索的同时显著提升了PSO算法的性能。此外,还将A-EIPSO在CEC2017基准测试套件上与5种先进的粒子群变体算法及2种主流的进化算法进行了比较。结果表明,A-EIPSO在不同类型函数上均表现出了优越的性能,改进了大多数优化问题的收敛精度,优于其他代表性的PSO变体算法和进化算法。

关键词: 粒子群优化算法, 自适应策略, 边界处理技术, 多种群, 精英交互学习

Abstract: Particle swarm optimization(PSO) algorithm relies on the cooperation between particles,which makes it show great intelligence in solving many optimization problems.However,due to the optimization mechanism,particles are easy to break through the boundary restrictions of the feasible region.If this behavior can have a clear guiding significance in the optimization process,it will help to improve the optimization performance of the algorithm.More importantly,the learning objects of particles in the original particle swarm optimization algorithm are mainly focused on the global optimal particles.This updating mechanism undoubtedly accelerates the loss of population diversity,and makes the population tend to fall into the local optimal.In order to further improve the population diversity and convergence accuracy when solving complex problems,an elite interactive learning particle swarm optimization algorithm(A-EIPSO) based on adaptive strategy is proposed.Firstly,the algorithm introduces a new bound-handling technique into the original PSO algorithm,and adaptively endows the distribution characteristics of particles in the solution space by using the historical location information and the distance of out of bounds particles,so as to modify the position of particles to meet the requirements of effectively handling out of violated particles.Then,based on multi-swarm technology,an elites learning strategy is designed to promote the exchange of social information among subswarms,and the elite particles instead of the global optimal particles guide the optimization behavior of particles in each subswarm.Experimental results show that,in most cases,the adaptive strategy can ensure that particles can achieve uniform exploration in the search space and significantly improve the performance of PSO algorithm.In addition,A-EIPSO is compared with five advanced particle swarm optimization variant algorithms and two mainstream evolutionary algorithms on the CEC2017 benchmark suite.The results show that A-EIPSO has superior performance on different types of functions,improves the convergence accuracy of most optimization pro-blems,and is superior to other representative PSO variant algorithms and evolutionary algorithms.

Key words: Particle swarm optimization algorithm, Adaptive strategy, Bound-handling techniques, Multi-swarm, Elite Interactive learning

中图分类号: 

  • TP301.6
[1]EBERHART R C,KENNEDY J.A new optimizer using particle swarm theory [C]//Proceedings of International Symposium on Micro Machine and Human Science(ISMMHS’95).Nagoya,Japan,1995:39-43.
[2]EBERHART R C,KENNEDY J.Particle swarm optimization[C]//Proceedings of IEEE International Conference on Neural Network(CNN’95).Perth,Australia,1995:1942-1948.
[3]HUANG K Y.A hybrid particle swarm optimization approach for clustering and classification of datasets[J].IEEE Transactions on Power Systems,2011,24(3):420-426.
[4]YAN T T,LI Z.Intelligent skin cancer detection using enhanced particle swarm optimization[J].IEEE Transactions on Evolutionary Computation,2018,158(20):118-135.
[5]HO S Y,LIN H S,LIAUH W H.OPSO:Orthogonal particle swarm optimization and its application to task assignment pro-blems[J].IEEE Transactions on Systems Man Cybernetics-Systems,B,2008,38(2):288-289.
[6]ZHANG S,XU J,LEE L H.Optimal computing budget allocation for particle swarm optimization in stochastic optimization[J].IEEE Transactions on Evolutionary Computation,2017,21(2):206-219.
[7]YANG X S,DEB S,ZHAO Y X,et al.Swarm intelligence:past,present and future[J].Soft Computing,2018,22(14):5923-5933.
[8]HELWIG S,WANKA R.Theoretical analysis of initial particle swarm behavior [C]//Proceedings of the International Confe-rence on Parallel Problem Solving from Nature.DBLP,PPSN,2008:889-898.
[9]KADIRKAMANATHAN V,SELVARAJAH K,FLEMING PJ.Stability analysis of the particle dynamics in particle swarm optimizer[J].IEEE Transactions on Evolutionary Computation,2006,10(3):245-255.
[10]WEI B,XIA X,YU F,et al.Multiple adaptive strategies based particle swarm optimization algorithm[J].Swarm and Evolutionary Computation,2020,57(23):100731.
[11]XU S,RAHMAT-SAMII Y.Boundary conditions in particleswarm optimization revisited[J].IEEE Transactions on Antennas and Propagation,2007,55(3):760-765.
[12]LAMPINEN J.A constraint handling approach for the differential evolution algorithm [C]//Proceedings of the Congress on Evolutionary Computation.2002:1468-1473.
[13]JUAREZ-CASTILLO E,ACOSTA-MESA H G,MEZURA-MONTES E.Empirical study of bound constraint-handling methods in Particle Swarm Optimization for constrained search spaces [C]//Evolutionary Computation.IEEE,2017:604-611.
[14]ZHANG W J,XIE X F,BI D C.Handling boundary constraints for numerical optimization by particle swarm flflying in periodic search space [C]//Proceedings of the IEEE Congress on Evolutionary Computation.2004:2307-2311.
[15]HELWIG S,BRANKE J,MOSTAGHIM S.Experimental ana-lysis of bound handling techniques in Particle Swarm Optimization[J].IEEE Transactions on Evolutionary Computation,2013,17(2):259-271.
[16]BOSE D,BISWAS S,KUNDU S,et al.A strategy pool adaptive artificial bee colony algorithm for dynamic environment through multi-population approach [C]//Proceedings of the Interna-tional Conference on Swarm,Evolutionary,and Memetic Computing.2012:611-619.
[17]PERAM T,VEERAMACHANENI K,MOHAN C K.Fitness-distance-ratio based particle swarm optimization [C]//Procee-dings of the 2003 IEEE Swarm Intelligence Symposium.2003:174-181.
[18]LIANG J J,SUGANTHAN P N.Dynamic multi-swarm particle swarm optimizer[C]//Proceedings of the IEEE Swarm Intelligence Symposium.2005:124-129.
[19]LYNN N,SUGANTHAN.Heterogeneous comprehensive lear-ning particle swarm optimization with enhanced exploration and exploitation[J].Swarm & Evolutionary Computation,2015,24(5):11-24.
[20]LIANG B,ZHAO Y,LI Y.A hybrid particle swarm optimization with crisscross learning strategy[J].Engineering Applications of Artificial Intelligence,2021,105(3):104418.
[21]KARABOGA D,BASTURK B.A powerful and efficient algorithm for numerical function optimization:Artificial bee colony(ABC) algorithm[J].Journal of Global Optimization,2007,39(3):459-471.
[22]GANDOMI A H,KASHANI A R.Evolutionary bound con-straint handling for particle swarm optimization[C]//International Symposium on Computational & Business Intelligence.IEEE,2016:148-152.
[23]GANDOMI A H,KASHANI A R,ZEIGHAMI F.Retainingwall optimization using interior search algorithm with different bound constraint handling[J].International Journal for Numerical & Analytical Methods in Geomechanics,2017,41(11):1304-1331.
[24]LIANG J J,SUGANTHAN P N.Dynamic multi-swarm particle swarm optimizer[C]//Proceedings of the IEEE Swarm Intelligence Symposium.IEEE,2005:124-129.
[25]RODRIGUEZ A,LAIO A.Clustering by fast search and find of density peaks[J].Science,2014,344(6191):1492-1496.
[26]GONG Y J,LI J J,ZHOU Y C,et al.Genetic learning particle swarm optimization[J].IEEE Transactions on Cybernetics,2016,46(10):2277-2290.
[27]AWAD N H,ALI M Z,LIANG J J,et al.Problem Defifinitions and Evaluation Criteria for the CEC 2017 Special Session and Competition on Single Objective Real-Parameter Numerical Optimization[R].Jordan University of Science and Technology,and Zhengzhou University,Technical report,Nanyang Technological University,2017.
[28]BRATTON D,KENNEDY J.Defifining a standard for particle swarm optimization [C]//Proceedings IEEE Swarm Intelligence Symposium.2007:120-127.
[29]TANABE R,FUKUNAGA A.Success-history based parameter adaptation for differential evolution[C]//2013 IEEE Congress on Evolutionary Computation.2013:71-78.
[30]WANG S,LIU G,GAO M,et al.Heterogeneous comprehensive learning and dynamic multi-swarm particle swarm optimizer with two mutation operators[J].Information Sciences,2020,540(12):175-201.
[31]DERRAC J,GARCÍA S,MOLINA D,et al.A practical tutorial on the use of non-parametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms[J].Swarm and Evolutionary Computation,2011,1(1):3-18.
[32]DERRAC J,GARCIA S,MOLINA D.A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms[J].Swarm & Evolutionary Computation,2011,1(1):3-18.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
渝ICP备16013121号-4
地址：重庆市渝北区洪湖西路18号    邮编：401121  
电话：023-63500828（编辑部） 023-67039612（会议/专题） 023-67039623（财务/发票）
E-mail：jsjkx12@163.com
本系统由北京玛格泰克科技发展有限公司设计开发