融合多策略改进蛇优化算法及其应用OA

In allusion to the problems that snake optimizer(SO)algorithm is prone to getting stuck in local optima and has slow convergence speed,a multi-strategy improved SO(MSSO)algorithm is proposed.The population is initialized by means of Cubic mapping to enhance its diversity.The global and local searches of the adaptive threshold balancing algorithm are balanced,and the global search ability is strengthened by means of the sine cosine strategy.Differential mutation and golden sine strategies are introduced to improve the structure of the algorithm,and the population restart mechanism improving the differential mutation is proposed to enhance the algorithm 's ability to escape from local optima.The lens reverse learning strategy is used to update the optimal solution and accelerate the convergence speed of the algorithm.The testing results of the improved algorithm on 10 benchmark functions show that the improved algorithm has significant advantages compared with other algorithms.The algorithm was applied to path planning by establishing a grid map.The experimental results show that the MSSO algorithm has achieved comprehensive performance improvement,verifying the feasibility and superiority of the algorithm in path planning problems.