Fuzzy C-mean clustering algorithm based on genetic simulated annealing algorithm battlefield site selection

In this paper, for a battlefield material point siting problem, we do not adopt the traditional ideas, but treat it as a classification and solution module model, and choose to bring in a nested algorithm model to fuzzy cluster the points on the battlefield in order to divide them into multiple independent siting problems, and change the aggregation path of each node according to a heuristic algorithm to traverse all reach possibilities as much as possible. Dividing the battlefield area into n regions for clustering optimization: the fuzzy C-mean clustering algorithm using genetic simulated annealing algorithm for fuzzy clustering within the battlefield area, and then the immune algorithm to make a selection of all possibilities, can guarantee the accuracy rate while greatly reducing the model running time and achieving higher efficiency. (1) regional selection stage, based on the difficulty of attacking each point of the battlefield to establish an evaluation index system, the use of cluster analysis methods[1] to divide the battlefield area into n categories to achieve for different looking for the corresponding command center, material center target group; (2) Immune algorithm optimization, based on the immune algorithm to construct the corresponding model and design a modified genetic algorithm to optimize the optimal addressing[2]. After using the genetic algorithm with adaptive parameters, a more stable centroid is obtained in 500 iterations. Based on the above two-stage planning method, the empirical study results show that it can provide scientific personalized optimal addressing points for addressing class problems.