混合改进策略的黑猩猩优化算法及其机械应用

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,进一步验证了SLWChOA的可行性和适用性.

Chimp optimization algorithm based on hybrid improvement strategy and its mechanical application

Chimp optimization algorithms(ChOA) based on the convex lens imaging strategy is proposed to overcome the drawbacks of easily trapping into local optimum, slow convergence speed and low optimization precision. Firstly, the population is initialized by the Sobol sequence, which can increase the randomness and diversity of the population, and lay the foundation for the global optimization of the algorithm. Then, the opposition-based learning strategy based on convex lens imaging is introduced, which is applied 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, the Wilcoxon rank sum test and some parts 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.