Chimp Optimization Algorithm Improved by Chaos Elite Pool Collaborative Teaching-Learning and Its Mechanical Application

doi:10.3778/j.issn.1002-8331.2110-0273

Abstract

Abstract: Chimp optimization algorithm improved by the elite chaos pool collaborative teaching-learning is proposed overcome the drawbacks of weak global search ability, low optimization accuracy, and slow convergence speed. This paper uses the chaotic elite pool strategy to generate the initial population, enhances the quality of the initial solution and the diversity of the population, and lays the foundation for the global optimization of the algorithm. Furthermore, adaptive oscillation factors are introduced to balance ChOA’s global exploration and local development capabilities. Finally, combining the teaching phase of the teaching and learning optimization algorithm and the individual memory idea of the particle swarm optimization algorithm to optimize the population position update process, and improve the algorithm’s optimization accuracy and convergence speed. The simulation experiment compares ECTChOA with standard ChOA, other meta-heuristic optimization algorithms, and the latest improved ChOA under 12 benchmark functions. The experimental results and the Wilcoxon rank sum test [p]-value results both show that the improved algorithm has higher search accuracy, faster convergence speed and better robustness. In addition, ECTChOA is applied to mechanical engineering design cases to further verify the feasibility and applicability of ECTChOA in actual engineering problems.

Key words: chimp optimization algorithm, chaos elite pool, teaching-learning based optimization, particle swarm optimization algorithm, adaptive oscillation factor, mechanical engineering design

摘要： 针对黑猩猩优化算法存在全局搜索能力弱、寻优精度低、收敛速度慢等问题，提出一种混沌精英池协同教与学改进的黑猩猩优化算法（chimp optimization algorithm improved by the elite chaos pool collaborative teaching-learning，ECTChOA）。采用混沌精英池策略生成初始种群，增强初始解的质量和种群的多样性，为算法全局寻优奠定基础；引入自适应振荡因子平衡ChOA的全局探索和局部开发能力；结合教与学优化算法的教学阶段和粒子群优化算法的个体记忆思想优化种群位置更新过程，提高算法的寻优精度和收敛速度。仿真实验将ECTChOA与标准ChOA、其他元启发式优化算法和最新改进ChOA在12个基准测试函数下进行寻优对比，实验结果与Wilcoxon秩和检验p值结果均表明所提改进算法具有更高搜索精度、更快的收敛速度和更好的鲁棒性。另外，将ECTChOA应用于机械工程设计案例中，进一步验证ECTChOA在实际工程问题中的可行性和适用性。

关键词: 黑猩猩优化算法, 混沌精英池, 教与学优化算法, 粒子群优化算法, 自适应振荡因子, 机械工程设计

LUO Shihang, HE Qing. Chimp Optimization Algorithm Improved by Chaos Elite Pool Collaborative Teaching-Learning and Its Mechanical Application[J]. Computer Engineering and Applications, 2023, 59(6): 299-309.

罗仕杭, 何庆. 混沌精英池协同教与学改进的ChOA及其应用[J]. 计算机工程与应用, 2023, 59(6): 299-309.

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