PLA Army Academy of Artillery and Air Defense
Complex combinatorial optimization problems can be solved by genetic algorithm, but there are also two shortcomings, one is the search efficiency is lower than other optimization algorithms, the other is easy to premature convergence and fall into local optimum. To solve these problems, the chaos "micro variation" genetic algorithm is proposed in this paper. The chaos optimization algorithm has the characteristics of randomness and ergodicity, which solves the premature problem that genetic algorithm is easy to fall into the local optimal solution, and makes the new algorithm have strong local search ability and the ability to complete the global search for the optimal solution. At the same time, the selection operator of genetic algorithm is added chaos disturbance, the crossover operator and mutation operator are adjusted adaptively, and the fitness function is improved. Based on the above, the overall performance of genetic algorithm is improved. Finally, the chaotic "micro mutation" genetic algorithm has faster evolution speed and higher search accuracy than the general chaotic genetic algorithm and classical genetic algorithm through the classical function verification.