混合改进策略的黑猩猩优化算法及其机械应用
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作者单位:

1.贵州大学大数据与信息工程学院;2.贵州

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TP301

基金项目:

国家自然科学基金项目(62166006);贵州省科技计划项目重大专项项目(黔科合重大专项字[2021] 335, 黔科合重大专项字[2018] 3002);贵州省科学技术厅(黔科合基础-ZK[2021] 一般335)


Chimp optimization algorithm based on hybrid improvement strategy
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College of Big Data and Information Engineering,Guizhou University

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    摘要:

    针对黑猩猩优化算法存在易陷入局部最优、收敛速度慢、寻优精度低等缺陷,提出了混合改进策略的黑猩猩优化算法(SLWChOA)。首先,利用Sobol序列初始化种群,增加种群的随机性和多样性,为算法全局寻优奠定基础;其次,引入基于凸透镜成像的反向学习策略,将其应用到当前最优个体上产生新的个体,提高算法的收敛精度和速度;同时,将水波动态自适应因子添加到攻击者位置更新处,增强算法跳出局部最优的能力;最后,通过10个基准测试函数、Wilcoxon秩和检验以及部分CEC2014函数进行仿真实验来评价改进算法的寻优性能,实验结果表明所提算法在寻优精度、收敛速度和鲁棒性上均较对比算法有较大提升。另外,通过一个机械优化设计实验进行测试分析,进一步验证SLWChOA的可行性和适用性。

    Abstract:

    Chimp optimization algorithms based on convex lens imaging strategy is proposed overcome the drawbacks of easily trapping into local optimum, slow convergence speed and low optimization precision. Firstly, the population was initialized by the Sobol sequence, which increase the randomness and diversity of the population, and lay the foundation for the global optimization of the algorithm. Then, introduce opposition-based learning strategy based on convex lens imaging applying it to the current optimal individual to generate new individuals, and improve the convergence accuracy and speed of the algorithm. At the same time, the water wave dynamic adaptive factor is added to the attacker’s location update to enhance the ability of the algorithm to escape from the local optimum. Finally, The Simulation experiments are conducted on the 10 benchmark functions, Wilcoxon rank sum test and some part of CEC2014 functions to evaluate the optimization performance of the improved algorithm. The experimental results show that the proposed algorithm has more significant improvement in optimization accuracy, convergence speed and robustness than the comparison algorithm. In addition, three mechanical optimization design experiments are conducted to test and analyze the feasibility and applicability of the improved algorithm.

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历史
  • 收稿日期:2021-06-26
  • 最后修改日期:2021-10-11
  • 录用日期:2021-10-27
  • 在线发布日期: 2021-12-01
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