Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (8): 237-246.doi: 10.11896/jsjkx.210700150

• Artificial Intelligence • Previous Articles     Next Articles

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).

PDF (PC)

2554

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

CLC Number: 

  • 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] PENG Yong,LIN Hu,PU Xiao-fei. Good Point Set Genetic Algorithm with Zooming Factor [J]. Computer Science, 2010, 37(11): 194-198.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
渝ICP备16013121号-4
Add:18# Honghu West Rd., Yubei ,Chongqing,China    Post Code: 401121
Tel: 023-63500828/023-67039612/023-67039623
E-mail: jsjkx12@163.com
Powered by Beijing Magtech Co., Ltd.