A Novel Elitism Co-Evolutionary Algorithm for Antagonistic Weapon-Target Assignment

The Antagonistic Weapon-Target Assignment (AGWTA) problem is a crucial decision issue in Command & Control (C2). Since this is a minimax problem, co-evolutionary algorithms can be used to solve it effectively. However, the co-evolutionary algorithm is originally designed for continuous minimax problems which loses its efficiency to discrete contexts. In this paper, a novel elitism co-evolutionary algorithm is proposed to solve the AGWTA. Firstly, an improved AGWTA model for air combat based on the attack and evasion strategies is proposed. Secondly, an elite cooperative genetic algorithm based on the framework of the co-evolutionary algorithm is put forward. In this proposed algorithm, a problem-specific coding method and evolution operator are designed. Meanwhile, an elite individual update mechanism is presented. Finally, based on the analysis of the relationship between the feasible solutions under the air combat environment, an evaluation index is proposed. Experiments show that the proposed algorithm has higher accuracy than traditional co-evolutionary algorithms for solving AGWTA problems.