(1) To solve the traditional locust

(1) To solve the traditional locust algorithm's optimization problem, the initial population generated by the random method will have an uneven distribution of the initial population and reduced initial population diversity. This paper combines the reverse strategy and chaotic logistic mapping. The locust algorithm population is initialized to enhance the diversity of the population and improve the solution efficiency, laying the foundation for the algorithm to perform global search diversity. (2) To balance the global exploration ability of the grasshopper algorithm and local optimization, and inertia weight algorithm based on the cloud model is introduced into the locust algorithm. The population is divided into three categories: excellent subgroupS, common subgroups, and poor subgroups. When locust is in a common subset, it can be adjusted by the cloud model. Each updates the position by updating the weight strategy, which can accelerate the convergence of the population and effectively prevent the population from falling into the local optimum.(3) To reduce the probability that the traditional whale algorithm is easy to fall into the local optimum, this paper uses the optimal individual chaotic search strategy to optimize the algorithm at the later stage of iteration, avoid the local optimization, and establish the optimal fitness value.