Qin Song, Liu Junling, Bai Xiaobo, Hu Gang
School of Art and Design, Xi'an University of Technology, Xi'an 710054, China.
National Demonstration Center for Experimental Arts Education, Nankai University, Tianjin 300371, China.
Biomimetics (Basel). 2024 Aug 8;9(8):478. doi: 10.3390/biomimetics9080478.
Based on a meta-heuristic secretary bird optimization algorithm (SBOA), this paper develops a multi-strategy improvement secretary bird optimization algorithm (MISBOA) to further enhance the solving accuracy and convergence speed for engineering optimization problems. Firstly, a feedback regulation mechanism based on incremental PID control is used to update the whole population according to the output value. Then, in the hunting stage, a golden sinusoidal guidance strategy is employed to enhance the success rate of capture. Meanwhile, to keep the population diverse, a cooperative camouflage strategy and an update strategy based on cosine similarity are introduced into the escaping stage. Analyzing the results in solving the CEC2022 test suite, the MISBOA both get the best comprehensive performance when the dimensions are set as 10 and 20. Especially when the dimension is increased, the advantage of MISBOA is further expanded, which ranks first on 10 test functions, accounting for 83.33% of the total. It illustrates the introduction of improvement strategies that effectively enhance the searching accuracy and stability of MISBOA for various problems. For five real-world optimization problems, the MISBOA also has the best performance on the fitness values, indicating a stronger searching ability with higher accuracy and stability. Finally, when it is used to solve the shape optimization problem of the combined quartic generalized Ball interpolation (CQGBI) curve, the shape can be designed to be smoother according to the obtained parameters based on MISBOA to improve power generation efficiency.
基于元启发式秘书鸟优化算法(SBOA),本文开发了一种多策略改进秘书鸟优化算法(MISBOA),以进一步提高工程优化问题的求解精度和收敛速度。首先,采用基于增量PID控制的反馈调节机制,根据输出值更新整个种群。然后,在捕猎阶段,采用黄金正弦引导策略提高捕获成功率。同时,为保持种群多样性,在逃逸阶段引入合作伪装策略和基于余弦相似度的更新策略。通过对求解CEC2022测试套件的结果进行分析,当维度设置为10和20时,MISBOA均获得了最佳综合性能。特别是当维度增加时,MISBOA的优势进一步扩大,在10个测试函数上排名第一,占总数的83.33%。这表明改进策略的引入有效地提高了MISBOA对各种问题的搜索精度和稳定性。对于五个实际优化问题,MISBOA在适应度值方面也具有最佳性能,表明其具有更强的搜索能力,精度更高且稳定性更好。最后,当将其用于求解组合四次广义鲍尔插值(CQGBI)曲线的形状优化问题时,基于MISBOA获得的参数可以将形状设计得更平滑,以提高发电效率。