电子学报 ›› 2019, Vol. 47 ›› Issue (10): 2177-2186.DOI: 10.3969/j.issn.0372-2112.2019.10.020

• 学术论文 • 上一篇    下一篇

精英反向黄金正弦鲸鱼算法及其工程优化研究

肖子雅, 刘升   

  1. 上海工程技术大学管理学院, 上海 201620
  • 收稿日期:2018-07-21 修回日期:2018-11-04 出版日期:2019-10-25 发布日期:2019-10-25
  • 通讯作者: 刘升
  • 作者简介:肖子雅 女,1994年10月出生于江苏徐州.2017进入上海工程技术大学攻读硕士学位,研究方向为智能算法、项目调度与优化.E-mail:m18817568572_1@163.com
  • 基金资助:
    国家自然科学基金(No.61673258,No.61075115);上海市自然科学基金(No.19ZR1421600)

Study on Elite Opposition-Based Golden-Sine Whale Optimization Algorithm and its Application of Project Optimization

XIAO Zi-ya, LIU Sheng   

  1. School of Management, Shanghai University of Engineering Science, Shanghai 201620, China
  • Received:2018-07-21 Revised:2018-11-04 Online:2019-10-25 Published:2019-10-25

摘要: 针对鲸鱼优化算法(Whale Optimization Algorithm,WOA)存在的收敛速度慢、寻优稳定性不足等问题,本文提出了精英反向学习的黄金正弦鲸鱼优化算法(Elite Opposition-Based Golden-Sine Whale Optimization Algorithm,EGolden-SWOA).利用精英反向学习策略提高种群的多样性和质量可以有效提升算法的收敛速度,同时引入黄金分割数优化WOA的寻优方式,从而协调算法的全局探索与局部开发能力.对20个单模态和多模态测试函数进行寻优实验,并与RLPSO(Reverse-learning and Local-learning Particle Swarm Optimization)、IWOA(Improved Whale Optimization Algorithm based on nonlinear convergence factor)等多个算法进行对比,实验结果表明EGolden-SWOA具有更好的寻优精度和稳定性.进一步对EGolden-SWOA进行求解大规模问题的实验,实验结果表明EGolden-SWOA可以有效解决大规模优化问题.最后将EGolden-SWOA应用于压力容器和蝶形弹簧设计优化问题,结果表明EGolden-SWOA在工程优化方面的性能优于RCSA(Rough Crow Search Algorithm)、CPSO(Co-evolutionary Particle Swarm Optimization)等改进算法,可以有效运用于实际工程优化问题.

关键词: 鲸鱼优化算法, 精英反向学习, 黄金正弦算法, 大规模优化问题, 工程优化

Abstract: In order to improve the slow convergence rate and low stability of WOA,elite opposition-based golden-sine whale optimization algorithm is proposed.Elite opposition-based learning strategy is used to improve the diversity and quality of the population so that the convergence rate can be promoted.At the same time,golden ratio is introduced to improve the optimal method of WOA,so as to ocoordinate the global exploration and local exploitation.Twenty unimodal and multimodal benchmark functions are tested and compared with that of other algorithms,and the experimental results show that EGolden-SWOA has a better performance in convergence rate and stability.The high dimensional function test shows that EGolden-SWOA perform well in solving large scale optimization problem.Finally,EGolden-SWOA is applied to the optimization design of the pressure vessel and tension/compression spring,the result shows that its performance in project optimization is better than RCSA and CPSO,it can be effectively applied to project optimization.

Key words: whale optimization algorithm(WOA), elite opposition-based learning, golden sine algorithm(Golden-SA), large scale optimization problem, project optimization

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