Adaptive Phase Switching Snake Optimizer Based on Orthogonal Learning and Joint Opposite Selection

An adaptive phase switching snake optimizer based on orthogonal learning and joint opposite selection (OJA-SO) is proposed to address the problems of poor inter activity in the optimization phase of the snake optimizer(SO), serious randomness of the initial population, and the tendency to fall into local optimal solutions. Firstly, an orthogonal matrix is used to initialize the snake population to make the distribution of individuals more uniform; secondly, an adaptive equation is designed to explore the development phase switching to replace the original food quantity and temperature threshold to make the algorithm perform adaptive phase switching; finally, a joint opposite selection strategy is used 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. Twenty benchmark test functions were selected for experiments on the OJA-SO algorithm to test the algorithm performance, and the Wilcoxon rank sum test was used to prove the algorithm significance. The results show that the OJA-SO algorithm improvement has been improved in terms of merit-seeking ability, robustness, and practicality.