计算机科学 ›› 2022, Vol. 49 ›› Issue (8): 237-246.doi: 10.11896/jsjkx.210700150

• 人工智能 • 上一篇    下一篇


陈俊, 何庆, 李守玉   

  1. 贵州大学大数据与信息工程学院 贵阳 550025
  • 收稿日期:2021-07-14 修回日期:2021-12-06 发布日期:2022-08-02
  • 通讯作者: 何庆(qhe@gzu.edu.cn)
  • 作者简介:(youngchen777@163.com)
  • 基金资助:

Archimedes Optimization Algorithm Based on Adaptive Feedback Adjustment Factor

CHEN Jun, HE Qing, LI Shou-yu   

  1. College of Big Data & Information Engineering,Guizhou University,Guiyang 550025,China
  • Received:2021-07-14 Revised:2021-12-06 Published:2022-08-02
  • About author:CHEN Jun,born in 1996,postgraduate.His main research interests include evolutionary computation and deep lear-ning.
    HE Qing,born in 1982,Ph.D,associate professor.His main research interests include big data application and evolutionary computation.
  • Supported by:
    Guizhou Province Science and Technology Plan Project Major Special Project(Qiankehe Major Special Project Word [2018] 3002,Qiankehe Major Special Project Word [2016] 3022),Guizhou Provincial Education Department Young Science and Techno-logy Talent Growth Project(Qiankehe KY Word [2016] 124),Guizhou University Cultivation Project(Qiankehe Platform Talent [2017]5788),Guizhou Provincial Public Big Data Key Laboratory Open Project (2017BDKFJJ004) and Guizhou Science and Technology Plan Project(Qiankehe Foundation-ZK[2021] General 335).

摘要: 针对基础阿基米德优化算法收敛速度慢、容易陷入局部最优的问题,文中提出了一种基于自适应反馈调节因子的阿基米德优化算法。首先,通过佳点集初始化种群,增强初始种群的遍历性,提高初始解的质量;其次,提出自适应反馈调节因子,平衡算法的全局探索与局部开发能力;最后,提出了莱维旋转变换策略,增加种群的多样性,以防止算法陷入局部最优。将所提算法与主流算法在14个基准测试函数以及部分CEC2014函数上进行30次比较实验,结果表明,所提算法的平均寻优精度、标准差以及收敛曲线均优于对比算法。同时将所提算法分别与对比算法在14个基准函数上进行Wilcoxon秩和检验,检验结果显示所提算法与对比算法的差异性显著。将所提算法应用于焊接梁设计问题,其相比原始算法提升了2%,验证了所提算法的有效性。

关键词: 阿基米德优化算法, 佳点集, 莱维飞行, 旋转变换算子, 自适应反馈调节因子

Abstract: Aiming at the problem of slow convergence speed of basic Archimedes optimization algorithm and is easy to fall into local optimum,this paper proposes an Archimedes optimization algorithm based on adaptive feedback adjustment factor.Firstly,initializing the population through the good point set to enhance the ergodicity of the initial population and improve the quality of the initial solution.Secondly,an adaptive feedback adjustment factor is proposed to balance the global exploration and local deve-lopment capabilities of the algorithm.Finally,the Levy rotation transformation strategy is proposed,to increase the diversity of the population and prevent the algorithm from falling into a local optimum.Comparative experiments of the proposed algorithm and mainstream algorithms are carried on 14 benchmark functions and some CEC2014 functions for 30 times.The optimization results of the algorithm on the function show that the average optimization accuracy,standard deviation and convergence curve of the proposed algorithm are better than that of the comparison algorithm.At the same time,Wilcoxon rank sum test is performed on 14 benchmark functions between the proposed algorithm and comparison algorithms.The test results show that the proposed algorithm is significantly different from comparison algorithms.It will be applied to the design of welded beams,which is 2% higher than the original algorithm,which verifies the effectiveness of the proposed algorithm.

Key words: Adaptive feedback adjustment factor, Archimedes optimization algorithm, Good point set, Levy fight, Rotation transformation strategy


  • TP301.6
[1]WANG Z M,DAI Y.A New Chaotic Genetic Hybrid Algorithm and Its Applications in Mechanical Optimization Design[J].Defence Technology,2010,6(3):220-224.
[2]MA Y,PING Y,GUO H,et al.Dynamic Economic Dispatch and Control of a Stand-alone Microgrid in DongAo Island[J].Journal of Electrical Engineering & Technology,2015,10(4):1433-1441.
[3]WILBURN B K,PERHINSCHI M G,WILBURN J N.A modified genetic algorithm for UAV trajectory tracking control laws optimization[J].International Journal of Intelligent Unmanned Systems,2014,2(2):58-90.
[4]VEKKOT S,GUPTA D,ZAKARIAH M,et al.Emotional Voice Conversion Using a Hybrid Framework With Speaker-Adaptive DNN and Particle-Swarm-Optimized Neural Network[J].IEEE Access,2020,8(1):74627-74647.
[5]WANG Y,DU T,LIU T,et al.Dynamic multiobjective squirrel search algorithm based on decomposition with evolutionary direction prediction and bidirectional memory populations[J].IEEE Access,2019,7:115997-116013.
[6]DEB K,PRATAP A,AGARWAL S,et al.A fast and elitist multiobjective genetic algorithm:NSGA-II[J].IEEE Transactions on Evolutionary Computation,2002,6(2):182-197.
[7]LI D,GUO W,LERCH A,et al.An adaptive particle swarm optimizer with decoupled exploration and exploitation for large scale optimization[J].Swarm and Evolutionary Computation,2021,60(7):100789-100721.
[8]ALJARAH I,FARIS H,MIRJALILI S.Optimizing connection weights in neural networks using the whale optimization algorithm[J].Soft Computing,2018,22(1):1-15.
[9]TANYILDIZI E.A novel optimization method for solving constrained and unconstrained problems:modified golden sine algorithm[J].Turkish Journal of Electrical Engineering & Compu-ter Sciences,2018,26(6):3287-3304.
[10]ARORA S,SINGH S.The Firefly Optimization Algorithm:Convergence Analysis and Parameter Selection[J].International Journal of Computer Applications,2014,69(3):48-52.
[11]JHAC D,LL B,YZC D.An improved multi-cores parallel artificial Bee colony optimization algorithm for parameters calibration of hydrological model-ScienceDirect[J].Future Generation Computer Systems,2018,81(22):492-504.
[12]BODHA K D,BODHA K.A Levy Flight Based Voltage Particle Swarm Optimization for Multiple-Objective Mixed Cost-Effective Emission Dispatch[C]//2018 8th International Conference on Cloud Computing,Data Science & Engineering(Confluence).IEEE,2018:82-87.
[13]MA C,ZHOU D Q,ZHANG Y.BP neural network water resources demand forecasting method based on improved whale algorithm [J].Computer Science,2020,47(S2):496-500.
[14]XIAO Z Y,LIU S.Research on elite reverse golden sine whale algorithm and its engineering optimization [J].Acta Electronica Sinica,2019,47(10):2177-2186.
[15]ZHANG J,LI X G.Research on intelligent production linescheduling problem based on levy firefly algorithm [J].Compu-ter Science,2021,48(S1):668-672.
[16]WOLPERT D H,MACREADY W G.No free lunch theorems for optimization[J].IEEE Trans on Evolutionary Computation,1997,1(1):67-82.
[17]HASHIM F A,HUSSAIN K,HOUSSEIN E H,et al.Archimedes optimization algorithm:a new metaheuristic algorithm for solving optimization problems[J].Applied Intelligence,2020,21(1):1-21.
[18]SUN X, WANG G, XU L,et al.Optimal estimation of the PEM fuel cells applying deep belief network optimized by improved archimedes optimization algorithm[J].Energy,2021,237(1):121532-121544.
[19]HOUSSEIN E H, HELMY B E, REZK H,et al.An enhanced Archimedes optimization algorithm based on Local escaping operator and Orthogonal learning for PEM fuel cell parameter identification[J].Engineering Applications of Artificial Intelligence,2021,103(1):104309-104321.
[20]LI Y, ZHU H, WANG D,et al.Comprehensive optimization of distributed generation considering network reconstruction based on Archimedes optimization algorithm[C]//IOP Conference Series:Earth and Environmental Science.IOP Publishing,2021,647(1):012031-012043.
[21]CHEN W W,NIE Y F,ZHANG W W,et al.A fast local mesh generation method about high-quality node set[J].Jisuan Lixue Xuebao:Chinese Journal of Computational Mechanics,2012,29(5):704-709.
[22]XIAO C, CAI Z, WANG Y.Incorporating good nodes set principle into evolution strategy for constrained optimization[C]//Third International Conference on Natural Computation(ICNC 2007).IEEE,2007,4:243-247.
[23]NICKABADI A,EBADZADEH M M,SAFABAKHSH R.Anovel particle swarm optimization algorithm with adaptive inertia weight[J].Applied Soft Computing,2011,11(4):3658-3670.
[24]KAMARUZAMAN A F,ZAIN A M,YUSUF S M,et al.Levy Flight Algorithm for Optimization Problems A Literature Review[J].Applied Mechanics & Materials,2013,421(1):496-501.
[25]ZHOU X J,YANG C H,GUI W H.Principle and development of state transition algorithm[J].Acta Automatica Sinica,2020,46(11):2260-2274.
[26]TARKHANEH O,ISAZADEH A,KHAMNEI H J.A new hybrid strategy for data clustering using cuckoo search based on Mantegna levy distribution,PSO and k-means[J].International Journal of Computer Applications in Technology,2018,58(2):137-149.
[27]GUPTA S,DEEP K.Random walk grey wolf optimizer for constrained engineering optimization problems[J].Computational Intelligence,2018,34(4):1025-1045.
[28]WANG J,YANG W,PEI D,et al.A novel hybrid forecasting system of wind speed based on a newly developed multi-objective sine cosine algorithm[J].Energy Conversion and Management,2018,163(1):134-150.
[29]TONG L,DONG M,AI B,et al.A Simple Butterfly Particle Swarm Optimization Algorithm with the Fitness-based Adaptive Inertia Weight and the Opposition-based Learning Average Elite Strategy[J].Fundamenta Informaticae,2018,163(2):205-223.
[30]LIN Y L.Robust estimation of parameter for fractal inverseproblem[J].Computers & Mathematics with Applications,2010,60(7):2099-2108.
[31]ALMGREN A S,AGOGINO A M.A Generalization and Cor-rection of the Welded Beam Optimal Design Problem Using Symbolic Computation[J].Journal of Mechanical Design,1989,111(1):137-140.
[32]ZHANG Z,MENG Q C,XUE R,et al.New algorithm for solving nonlinear constrained optimization problems with particle swarm optimizer[J].Journal of Harbin Institute of Technology,2006,38(10):1716-1718.
[33]HRELJA M,KLANCNIK S,BALIC J,et al.Modelling of aTurning Process Using the Gravitational Search Algorithm[J].International Journal of Simulation Modelling,2014,13(1):30-41.
[34]ERGEZER M,SIMON D.Oppositional biogeography-based optimization for combinatorial problems[C]//Evolutionary Computation.IEEE,2011:1496-1503.
[35]MAYER D G,KINGHORN B P,ARCHER A A.Differentialevolution an easy and efficient evolutionary algorithm for model optimisation[J].Agricultural Systems,2005,83(3):315-328.
[36]DORIGO M,BIRATTARI M,STÜTZLE T.Ant Colony Optimization[J].IEEE Computational Intelligence Magazine,2006,1(4):28-39.
[37]BABAEI F,LASHKARI Z B,SAFARI A,et al.Salp swarm algorithm-based fractional-order PID controller for LFC systems in the presence of delayed EV aggregators[J].IET Electrical Systems in Transportation,2020,10(3):259-267.
[1] 范星泽, 禹梅.
Coverage Optimization of WSN Based on Improved Grey Wolf Optimizer
计算机科学, 2022, 49(6A): 628-631. https://doi.org/10.11896/jsjkx.210500037
[2] 章菊, 李学鋆.
Research on Intelligent Production Line Scheduling Problem Based on LGSO Algorithm
计算机科学, 2021, 48(6A): 668-672. https://doi.org/10.11896/jsjkx.210300118
[3] 郑洁锋, 占红武, 黄巍, 张恒, 吴周鑫.
Lévy Flight的发展和智能优化算法中的应用综述
Development of Lévy Flight and Its Application in Intelligent Optimization Algorithm
计算机科学, 2021, 48(2): 190-206. https://doi.org/10.11896/jsjkx.200500142
[4] 郭启程, 杜晓玉, 张延宇, 周毅.
Three-dimensional Path Planning of UAV Based on Improved Whale Optimization Algorithm
计算机科学, 2021, 48(12): 304-311. https://doi.org/10.11896/jsjkx.201000021
[5] 李阳, 李维刚, 赵云涛, 刘翱.
Grey Wolf Algorithm Based on Levy Flight and Random Walk Strategy
计算机科学, 2020, 47(8): 291-296. https://doi.org/10.11896/jsjkx.190600107
[6] 张严, 秦亮曦.
Improved Salp Swarm Algorithm Based on Levy Flight Strategy
计算机科学, 2020, 47(7): 154-160. https://doi.org/10.11896/jsjkx.190600068
[7] 孙博文, 韦素媛.
DV-Hop Localization Algorithm Based on Grey Wolf Optimization Algorithm with
Adaptive Adjutment Strategy
计算机科学, 2019, 46(5): 77-82. https://doi.org/10.11896/j.issn.1002-137X.2019.05.012
[8] 李荣雨,戴睿闻.
Adaptive Step-size Cuckoo Search Algorithm
计算机科学, 2017, 44(5): 235-240. https://doi.org/10.11896/j.issn.1002-137X.2017.05.042
[9] 钱伟懿,候慧超,姜守勇.
New Self-adaptive Cuckoo Search Algorithm
计算机科学, 2014, 41(7): 279-282. https://doi.org/10.11896/j.issn.1002-137X.2014.07.058
[10] 彭勇,林浒,卜霄菲.
Good Point Set Genetic Algorithm with Zooming Factor
计算机科学, 2010, 37(11): 194-198.
[11] 程军盛 张铃.

计算机科学, 2002, 29(4): 67-68.
Full text



No Suggested Reading articles found!