多策略融合的蛇优化算法及其应用OACSTPCD

This paper proposed a multi-strategy snake optimizer to address the problems of poor interactivity in the optimiza-tion-seeking phase of the snake algorithm,serious randomness of the initial population,and the tendency to fall into local opti-mal solutions.Firstly,it used an orthogonal matrix to initialize the snake population to make the individuals more uniformly dis-tributed;secondly,it designed an adaptive equation to explore the development phase switching to replace the original food quantity and temperature threshold to make the algorithm perform adaptive phase switching;finally,it used a joint reverse selec-tion strategy to replace the original new individual hatching method of the algorithm to improve the convergence accuracy of the algorithm while accelerating the convergence efficiency of the algorithm.It selected ten benchmark test functions to experiment the MSSO algorithm from different perspectives to test the algorithm performance,analyzed the effectiveness of each strategy,and used the Wilcoxon rank sum test to prove the algorithm significance,and verified the practicality of MSSO through two en-gineering application simulation experiments.The results of each experiment show that MSSO performs better than the compara-tive algorithm comprehensively,which proves that the MSSO algorithm improvement has improved in the aspects of the search ability,robustness and practicality.