无迹西格玛点引导的拟反向黏菌算法及其工程应用

iming at the search stagnation and poor stability of the Slime Mould algorithm, propose an unscented sigma point guided quasi-opposite Slime Mould Algorithm. Firstly, use quasi-opposite learning and quasi-reflected learning to exploration and exploitation behaviors according to the original Slime Mould Algorithm to generate a comprehensive opposite population that includes both quasi-opposite learning and quasi-reflected learning, expand the search space. Secondly, according to the diversity of the population, decide whether to use the opposite population to regenerate a new population for subsequent calculations, avoid the continuous opposite process destroying the search characteristics of the population itself, and improve the search accuracy. Finally, use unscented transformation Sigma point to improve the basic movement mode of Slime Mould Algorithm, make the unscented sigma point guide the search, and accelerate the convergence speed. The experimental part uses the CEC2017 benchmark test functions, uses traditional statistical index and MAE ranking, Wilcoxon rank-sum test to verify the effectiveness of the algorithm, and use it to solve the car side impact design problem, compares and tests with the novel swarm intelligence algorithms, improved algorithms and incomplete algorithm. The experimental results show that the strategies is effective and combinations of strategies complement each other, and the improved algorithm's solution accuracy and robustness are more competitive.