计算机科学

计算机科学 ›› 2023, Vol. 50 ›› Issue (8): 209-220.doi: 10.11896/jsjkx.220500275

• 人工智能 • 上一篇    下一篇

基于柯西变异和差分进化的混沌白骨顶鸟算法

周雪荃1, 杜逆索2, 欧阳智2   

  1. 1 贵州大学数学与统计学院 贵阳 550025
    2 贵州省大数据产业发展应用研究院(贵州大学) 贵阳 550025
  • 收稿日期:2022-05-30 修回日期:2022-09-29 出版日期:2023-08-15 发布日期:2023-08-02
  • 通讯作者: 杜逆索(nsdu@gzu.edu.cn)
  • 作者简介:(zhouxq98@qq.com)
  • 基金资助:
    贵州省科学技术厅重大科技计划项目(黔科合重大专项字[2018]3002); 贵州大学培育项目(贵大培育[2020]41号)

Chaos COOT Bird Algorithm Based on Cauchy Mutation and Differential Evolution

ZHOU Xuequan1, DU Nisuo2, OUYANG Zhi2   

  1. 1 School of Mathematics and Statistics,Guizhou University,Guiyang 550025,China
    2 Guizhou Big Data Academy,Guizhou University,Guiyang 550025,China
  • Received:2022-05-30 Revised:2022-09-29 Online:2023-08-15 Published:2023-08-02
  • About author:ZHOU Xuequan,born in 1998,postgra-duate.Her main research interests include social simulation and intelligent algorithm.
    DU Nisuo,born in 1986,Ph.D,associate professor.His main research interests include data science,social simulation and machine learning.
  • Supported by:
    Major Scientific and Technological Special Project of Guizhou Province, China([2018]3002) and Cultivation Project of Guizhou University([2020]41).

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摘要： 针对白骨顶鸟优化算法(COOT)寻优精度低、容易陷入局部最优、收敛速度慢等问题,提出了基于柯西变异和差分进化的混沌白骨顶鸟算法(Logistic Chaos Coot bird algorithm based on Cauchy mutation and Differential evolution,CDLCOOT)。首先,通过柯西变异使白骨顶鸟位置发生扰动,扩大搜索范围,提高算法的全局搜索能力;其次,对领导者白骨顶鸟采取差分进化策略,增加种群多样性,使适应度更好的领导者带领种群寻优,引导白骨顶鸟个体向最优解前进,帮助其更快地搜索;最后,在白骨顶鸟进行链式运动时加入logistic混沌因子,从而实现混沌的链式跟随运动,提高算法跳出局部最优的能力。在12个经典的测试函数和9个CEC2017测试函数上进行仿真实验,将CDLCOOT算法与正余弦算法(SCA)、灰狼优化算法(GWO)、蚁狮优化算法(ALO)、黑洞模拟算法(MVO)等其他先进算法及原始COOT算法、具有单一策略的原算法进行对比,验证改进算法的有效性。实验结果表明,CDLCOOT算法相比其他启发式算法和改进算法具有更好的全局寻优能力和更快的收敛速度。在经典测试函数中,对于4个单模态函数,CDLCOOT算法寻优平均值相比原始算法平均提高了76个数量级;在2个多模态函数上寻到理论最优值,在另外2个多模态函数上寻优平均值分别比原始算法提高了三四个数量级;在4个固定维度多模态函数上,算法都能寻到理论最优值,收敛速度更快。在CEC2017测试函数中,所提算法在单模态、多模态和混合模态上的收敛精度相比原算法都有所提升,且其收敛速度也比原算法和其他算法更快,算法稳定性更高。

关键词: 白骨顶鸟算法, 柯西变异, 差分进化, Logistic混沌

Abstract: Aiming at the problems of low optimization accuracy,easy to fall into local optimization and slow convergence speed of COOT bird algorithm,a logistic chaos CDLCOOT algorithm based on Cauchy mutation and differential evolution is proposed.Firstly,the position of the COOT bird is disturbed by Cauchy mutation to expand the search range and improve the global search ability of the algorithm.Secondly,the differential evolution strategy is adopted for the leader COOT bird to increase the population diversity,so that the leader with better fitness can lead the population to search for the optimal solution,guide the individual COOT bird to move towards the optimal solution,and help it search faster.Finally,the logistic chaos factor is added to the chain movement of the COOT bird,so as to realize the chaotic chain following movement and improve the ability of the algorithm to jump out of the local optimum.Simulation experiments are carried out on 12 classical test functions and 9 CEC2017 test functions.The CDLCOOT algorithm is compared with other advanced algorithms,such as the sine cosine algorithm(SCA),gray wolf optimizer(GWO),ant lion optimizer(ALO),multi-verse optimizer(MVO),as well as original COOT bird algorithm and the original algorithm with single strategy to verify the effectiveness of the improved algorithm.Experimental results show that CDLCOOT has better global optimization ability and faster convergence speed than other heuristic algorithms and improved algorithms.In the classical test functions,the average value of the algorithm is 76 orders of magnitude higher than that of the original algorithm on the four unimodal functions.The theoretical optimal value is found on two multimodal functions,and the average value on other two multimodal functions is 3 or 4 orders of magnitude higher than the original algorithm.On the four fixed dimension multimodal functions,the algorithm can find the theoretical optimal value,and the convergence speed is faster.In CEC2017 test functions,the optimization accuracy of the algorithm in unimodal,multimodal and hybrid functions is improved compared with the original algorithm,and its convergence speed is also superior to the original algorithm and other algorithms,and the stability of the algorithm is better.

Key words: COOT bird algorithm, Cauchy mutation, Differential evolution, Logistic chaos

中图分类号: 

  • TP301
[1]MIRJALILI S,MIRJALILI S M,LEWIS A.Grey wolf optimizer[J].Advances inEngineering Software,2014,69:46-61.
[2]MIRJALILI S.Dragonfly algorithm:a new meta-heuristic optimization technique for solving single-objective,discrete,and multi-objective problems[J].Neural Computing and Applications,2016,27(4):1053-1073.
[3]MIRJALILI S,LEWIS A.The whale optimization algorithm[J].Advances in Engineering Software,2016,95:51-67.
[4]SAREMI S,MIRJALILI S,LEWIS A.Grasshopper optimisation algorithm:theory and application[J].Advances in Engineering Software,2017,105:30-47.
[5]ARORA S,SINGH S.Butterfly optimization algorithm:a novelapproach for global optimization[J].Soft Computing,2019,23(3):715-734.
[6]XUE J,SHEN B.A novel swarm intelligence optimization approach:sparrow search algorithm[J].Systems Science & Control Engineering,2020,8(1):22-34.
[7]NARUEI I,KEYNIA F.A new optimization method based onCOOT bird natural life model[J].Expert Systems with Applications,2021,183:115352.
[8]MEMARZADEH G,KEYNIA F.A new optimal energy storage system model for wind power producers based on long short term memory and Coot Bird Search Algorithm[J].Journal of Energy Storage,2021,44:103401.
[9]MAHDY A,HASANIEN H M,HELMY W,et al.Transientstability improvement of wave energy conversion systems connected to power grid using anti-windup-coot optimization strategy[J].Energy,2022,245:123321.
[10]HA P T,TRAN D T,NGUYEN T T.Electricity generationcost reduction for hydrothermal systems with the presence of pumped storage hydroelectric plants[J].Neural Computing and Applications,2022,34(12):9931-9953.
[11]HOUSSEIN E H,HASHIM F A,FERAHTIA S,et al.Battery parameter identification strategy based on modified coot optimization algorithm[J].Journal of Energy Storage,2022,46:103848.
[12]ALQAHTANI A S,SARAVANAN P,MAHESWARI M.et al.An automatic query expansion based on hybrid CMO-COOT algorithm for optimized information retrieval[J].The Journal of Supercomputing,2022,78:8625-8643.
[13]HUANG Y,ZHANG J,WEI W,et al.Research on Coverage Optimization in a WSN Based on an Improved COOT Bird Algorithm[J].Sensors,2022,22(9):3383.
[14]PAHADE J K,JHA M.A Hybrid Fuzzy-SCOOT Algorithm to Optimize Possibilistic Mean Semi-absolute Deviation Model for Optimal Portfolio Selection[J].International Journal of Fuzzy Systems,2022,24(4):1958-1973.
[15]WANG Q,HE Q,LIN J,et al.Chaos ant lion optimizer based on elite opposition-based learning with perturbation factor[J].Intelligent Computer and Applications,2020,10(8):51-57.
[16]MAO Q H,ZHANG Q.Improved Sparrow Algorithm Combining Cauchy Mutation and Opposition-Based Learning[J].Journal of Frontiers of Computer Science and Technology,2021,15(6):1155-1164.
[17]HE Z M,LI W J.Flower pollination algorithm based on dynamic global search and Cauchy mutation[J].Computer Engineering and Applications,2019,55(19):74-80,222.
[18]ZHAO Y T,CHEN J C,LI W G.Multi-objective Grey Wolf Optimization Hybrid Adaptive Differential Evolution Mechanism[J].Computer Science,2019,46(S2):83-88.
[19]LIN J,HE Q.Fusion Sine Cosine and Mutation Selection Grasshopper Optimization Algorithm[J].Journal of Chinese Compu-ter Systems,2021,42(4):706-713.
[20]HE Q,LIN J,XU H.Hybrid Cauchy mutation and uniform distribution of grasshopper optimization algorithm[J].Control and Decision,2021,36(7):1558-1568.
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