计算机科学

计算机科学 ›› 2023, Vol. 50 ›› Issue (11): 248-258.doi: 10.11896/jsjkx.221100143

• 人工智能 • 上一篇    下一篇

融合麻雀搜索和随机差分的双向学习平衡优化器算法

侯新宇1, 鲁海燕1,2, 卢梦蝶1, 徐杰1, 赵金金1   

  1. 1 江南大学理学院 江苏 无锡 214122
    2 无锡市生物计算工程技术研究中心 江苏 无锡 214122
  • 收稿日期:2022-11-16 修回日期:2023-02-07 出版日期:2023-11-15 发布日期:2023-11-06
  • 通讯作者: 鲁海燕(luhaiyan@jiangnan.edu.cn)
  • 作者简介:(hxyu2016@sohu.com)
  • 基金资助:
    国家自然科学基金 (61772013);江苏省青年基金(BK20190578)

Bidirectional Learning Equilibrium Optimizer Combining Sparrow Search and Random Difference

HOU Xinyu1, LU Haiyan1,2, LU Mengdie1, XU Jie1, ZHAO Jinjin1   

  1. 1 College of Science,Jiangnan University,Wuxi,Jiangsu 214122,China
    2 Wuxi Biological Computing Engineering Technology Research Center,Wuxi,Jiangsu 214122,China
  • Received:2022-11-16 Revised:2023-02-07 Online:2023-11-15 Published:2023-11-06
  • About author:HOU Xinyu,born in 1998,postgra-duate,is a member of China Computer Federation.His main research interest is optimization and control.LU Haiyan,born in 1970,Ph.D,asso-ciate professor,master supervisor.Her main research interests include combination optimization and intelligent algorithms.
  • Supported by:
    National Natural Science Foundation of China(61772013) and Youth Foundation of Jiangsu(BK20190578).

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摘要： 针对平衡优化器算法(Equilibrium Optimizer,EO)求解精度低、收敛速度慢等问题,提出一种融合麻雀搜索和随机差分的双向学习平衡优化器算法。首先,给出了基于麻雀搜索算法的自适应种群划分策略,以平衡算法的全局探索和局部勘探,从而提高算法的收敛精度和收敛速度。其次,引入随机差分策略来重建平衡池,增加个体之间的信息交流,以利于算法跳出局部最优。最后,设计了一种双向混沌反向学习策略并将其应用到更新后的种群,以增加种群多样性,从而进一步提高算法的收敛精度。通过14个测试函数进行仿真实验,使用Wilcoxon秩和检验以及平均绝对误差来评价算法性能,并将改进算法应用到两个工程设计问题,实验结果验证了3种改进策略的有效性,且改进算法的收敛精度、收敛速度和鲁棒性都有显著提高。

关键词: 平衡优化器算法, 双向混沌反向学习, 算法融合, 随机差分, 群智能优化算法

Abstract: To address the problems of low solution accuracy and slow convergence speed of equilibrium optimizer,a bidirectional learning equilibrium optimizer combining sparrow search and random difference is presented.Firstly,an adaptive population division strategy based on sparrow search algorithm is proposed to balance the global exploration and local exploitation of the algorithm,so as to improve the convergence accuracy and convergence speed of the algorithm.Secondly,a random difference strategy is introduced to reconstruct the equilibrium pool and to increase the information exchange between individuals,so as to facilitate the algorithm to jump out of the local optimum.Finally,a bidirectional chaotic opposition learning strategy is designed and applied to the updated population to increase the population diversity and hence to further improve the convergence accuracy of the algorithm.Simulation experiments are conducted with 14 test functions,the performance of algorithm is evaluated using Wilcoxon rank-sum test and mean absolute error,and the improved algorithm is applied to two engineering design problems.Experimental results show that the three improvement strategies are effective and the convergence accuracy,convergence speed and robustness of the improved algorithm are significantly enhanced.

Key words: Equilibrium optimizer, Bidirectional chaotic opposition learning, Algorithm fusion, Random difference, Swarm intelligence optimization algorithms

中图分类号: 

  • TP301
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