Combat Formation Based On Genetic Algorithm Optimization Study

On one hand, the optimization method for large-scale air combat formation tactics based on genetic algorithm is developed in this paper. A hierarchical formation tactics is employed in this method and the hierarchical code is introduced to encode formations. Then genetic algorithm improved by simulated annealing algorithm is used to optimize the engagement results of BVR (Beyond View Range) intercepts so that a better formation can be gained. This paper integrates two key ideas. The first is that performance reflected engagement outcome and formation advantage, and the second is using genetic algorithm for performance-based optimization of blue team formation tactics. This method may act as a reference for analyzing the advantage of formations and exploring new formations. At last two calculating examples are used to testify the effectiveness of this method.On the other hand, the property of genetic algorithm improved by simulated annealing algorithm for finding the best value is studied in this paper. Genetic algorithm is an adaptive global optimal algorithm that simulates the inheritance and evolution of the biology. The whole course of finding the best value depends on probability. It differs from other traditional optimal algorithms mainly in dealing with discrete points. Genetic algorithm, simulated algorithm and genetic algorithm improved by simulated annealing algorithm are described and their abilities of finding the best value are analyzed in this paper. At last, several examples of genetic algorithm improved by simulated annealing algorithm, which adopted different functions deciding whether to accept a state and different Markov chains, are used to analyze the property of this algorithm.