| Summary: | Aiming at the shortcoming of precocity and slow convergence in the application of traditional algorithms to solve the Weapon-Target Assignment (WTA) problem, this paper proposed an intuitionistic fuzzy genetic algorithm that combined with simulated annealing Meta-Lamarckian learning strategy and adaptive mutation to improve the efficiency and speed of solving WTA problem. Firstly, it considered the various constraint functions of WTA problem, in which make the threat of remaining targets minimum and the damage from attacks maximum, established the mathematical model. Next, it defined the membership and non-membership functions of object and constraint function, and built the intuitionistic fuzzy WTA model on the basis of the “min-max” operator. Then, this paper designed a strategy of Meta-Lamarckian learning for simulated annealing and adaptive mutation to enhance the capability of local search and the speed of upper convergence for the algorithm. Finally, this method is effective via the simulation and the analysis of comparison with GA, PSO.
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