Optimization of Empire Competition Algorithm Based on Gauss-Cauchy Mutation
Computer Science

Computer Science ›› 2021, Vol. 48 ›› Issue (11A): 142-146.doi: 10.11896/jsjkx.201200071

• Intelligent Computing • Previous Articles     Next Articles

Optimization of Empire Competition Algorithm Based on Gauss-Cauchy Mutation

WEI Xin, FENG Feng   

  1. School of Information Engeineering,Ningxia University,Yinchuan 750021,China
  • Online:2021-11-10 Published:2021-11-12
  • About author:WEI Xin,born in 1996,postgraduate.His main research interests include improvement of intelligent algorithm.
    FENG Feng,born in 1971,Ph.D,professor.His main research interests include information system engineering and application and so on.
  • Supported by:
    Major Projects of Ningxia Key Research and Development Plan(2018BFG02003) and Postgraduate Innovation Project of Ningxia University(GIP2020091).

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Abstract: In order to solve the problems of slow convergence speed and easy to fall into local optimum in the competition process of imperial competitive algorithm (ICA),a new imperial competitive algorithm based on Gauss-Cauchy mutation (GCICA) is proposed.Gauss mutation is introduced in ICA Empire competition to speed up the convergence speed in the competition process;after the Empire perishes,the diversity is reduced and only in a small area for optimization,and Cauchy mutation is introduced to make it jump out of local optimum.By analyzing the simulation results of the algorithm with Gauss,Cauchy and Gauss-Cauchy mutation on several typical benchmark functions,the convergence speed and optimization accuracy of GCICA are improved.

Key words: Cauchy mutation, Gauss mutation, Imperial competition algorithm, Optimization algorithm

CLC Number: 

  • TP301.6
[1]ATASHPAZ-GARGARI E,LUCAS C.Imperialist competi-tivealgorithm:an algorithm for optimization inspired by imperialistic competition[C]//Proceedings of the 2007 IEEECongress on Evolutionary Computation.Piscataway,NJ,USA:IEEE,2007:4661-4667.
[2]CAI Y G,WANG S H,QI Y H,et al.Imperial competitive algorithm for CVRP [J/OL].Computer Application Research.https://t.cnki.net/kcms/detail?v=oqvvWN29B9HX5eBR4OEUfUBh9t1KN44je6B9E6qG96qxku-1h3_o14CjP1UjnhLl_sRie0114R7SR7QFK1VtfSKRNrZUXyECp7ylgJCsBJpX8TfvDtxAc1KYVtAInZuM&uniplatform=NZKPT.
[3]TIAN F L,ZHANG E Z,PAN S R,et al.Transformer faultdiagnosis model based on eigenvector optimization and ica-svm[J].Power System Protection and Control,2019,47(17):163-170.
[4]LI R,LI X H,CHEN X.Research on reliability green logistics distribution location routing problem[J].Computer Engineering and Application,2020,56(23):237-244.
[5]ZHANG G H,LU X X,HU Y F,et al.Machine fault rescheduling in Flexible Job Shop Based on improved imperial competitive algorithm [J/OL].Computer Application.https://kns.cnki.net/kcms/detail/detail.aspx?dbcode=CJFD&dbname=CJFDLAST2021&filename=JSJY202108011&uniplatform=NZKPT&v=5lc3RO%25mmd2BEUUAnKQC5rEW2%25mmd2BEXwoBG5wiOW0baHXidAcCuqD2JXrCmJGO51VWS0nWG5.
[6]ATASHPAZ-GARGARI E.Imperialist competitive alg-orithm.(ICA)[CP/OL].[2012-09-18].http://www.Math-works.com/matlabcentral/fileexchange/22046-imperialist-competitive-algorithm-ica.
[7]WANG G L,LI B.Improved algorithm of Empire competitioninspired by the historical facts of spring and autumn and Warring States[J].Computer Applications,2021,41(2):470-478.
[8]GUO W Q,YE D Y.Optimization of Empire competition algorithm based on Empire splitting[J].Computer Applications,2013,33 (S2):86-90.
[9]DAVOODI E,BABAEI E,MOHAMMADI-IVATLOO B.Imperialist Competitive Algorithm with Effective Assimilation Strate-gy:A Comparative Study on Numerical Benchmark Functions[J].IETE Journal of Research,2020,66.
[10]BARKHODA W,SHEIKHI H.Immigrantimp-erialist competitive algorithm to solve the multico-nstraint node placement problem in target-based wireless sensor networks[J].Ad Hoc Networks,2020,106.
[11]ZHANG H,ZANG G Q.Fault tolerant scheduling mechanism for real-time tasks based on improved ICA algorithm[J].Journal of Henan University (Natural Science Edition)),2015,45(5):604-611.
[12]CHEN M H,LIU J L,XU J F,et al.Diversified search Empire competition algorithm for traveling salesman problem[J].Computer Applications,2019,39(10):2992-2996.
[13]WANG H.improved intelligent algorithm.Research on hybridparticle swarm optimization algorithm based on Cauchy mutation[D].Wuhan:China University of Geosciences,2008.
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