Abstract
Weapon-target assignment (WTA) which is crucial in cooperative air combat explores assigning weapons to targets with the objective of minimizing the threats from those targets. Based on threat functions, there are four WTA models constrained by the payload and other tactical requirements established. The improvements of ant colony optimization are integrated with respect to the rules of path selection, pheromone update, and pheromone concentration interval, and algorithm AScomp is proposed based on the elite strategy of ant colony optimization (ASrank). We add garbage ants to ASrank; when the pheromone is updated, the elite ants are rewarded and the garbage ants are punished. A WTA algorithm is designed based on the improved ant colony optimization (WIACO). For the purpose of demonstration of WIACO in air combat, a real-time WTA simulation algorithm (RWSA) is proposed to provide the results of average damage, damage rate, and kill ratio. The following conclusions are drawn: (1) the third WTA model, considering the threats of both sides and hit probabilities, is the most effective among the four; (2) compared to the traditional ant colony algorithm, the WIACO requires fewer iterations and avoids local optima more effectively; and (3) WTA is better conducted when any fighter is shot down or any fighter’s missiles run out than along with the flight.
Highlights
Weapon-target assignment (WTA) is a dynamic multivariable and multiconstraint problem, which is characterized by antagonism, initiative, and uncertainty
This paper focuses on the WTA modeling, solution, and simulation in air combat scenario
Model 3 is proposed for the first time considering the threats of both sides and hit probabilities
Summary
Weapon-target assignment (WTA) is a dynamic multivariable and multiconstraint problem, which is characterized by antagonism, initiative, and uncertainty. SA starts from a high initial temperature, and as the temperature falls down, the global optimal solution is found randomly; even when the searching falls into a local optimal solution, SA has a probability to jump out and eventually goes to the global optimum This algorithm converges slowly and takes time. The basic ant colony algorithm was applied to the target assignment problem of the air defense C3I systems by Huang et al [16] in 2005. In 2017, Li et al [19] designed a biobjective WTA optimization model which maximized the expected damage of the enemy and minimized the cost of missiles; a modified Pareto ant colony optimization algorithm was used in the solution, which produced better results than two multiobjective optimization algorithms NSGA-II and SPEA-II. It is concluded that WIACO requires fewer iterations than traditional ant colony algorithm, and it avoids local optima more effectively.
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