更新阶段分为两个阶段,第一阶段使用云模型惯性权重，第二阶段使用最优个体

Update phase is divided into two phases, the first to use cloud model inertia weight, second stage uses the best individual chaotic search strategy. The first stage, inertia weight locust cloud model weight individuals in a population will be divided into subgroups good, normal and poor subgroups subgroup, inertial weights of different sizes to increase the convergence speed and reuse expanded search. Mathematical model of corrected can be expressed as the following equation:

The update phase is divided into two stages, the first using the inertiaweight of the cloud model and the second using the optimal individual chaos search strategy. In the first stage, the locust population is divided into fine subgroups, common subgroups and poor subgroups by using the inertial weight of cloud model, and the inertiaweight of different sizes is used to improve the convergence speed and expand the search range. The modified mathematical model can be represented as the following formula:

The first stage uses the inertia weight of cloud model, and the second stage uses the optimal individual chaos search strategy. In the first stage, using the inertia weight of cloud model, the grasshopper population can be divided into excellent sub group, ordinary sub group and poor sub group. Different inertia weights are used to improve the convergence speed and expand the search range. The modified mathematical model can be expressed as the following formula:<br>