Improved Ant Colony Optimization for Weapon-Target Assignment

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.

Decentralized Algorithms for Weapon-Target Assignment in Swarming Combat System

“…where x ij is a binary variable that indicates whether W i is assigned to T j . If W i is assigned to T j , then x ij � 1, otherwise, x ij � 0. e constraint (2) guarantees that a weapon is assigned to exactly one target at a time. e constraint (3) states that a target is attacked by at least one weapon.…”

“…then the new desired target is selected, wherein f i denotes the value calculated by using equation (1). Otherwise, we repeat procedures (1) and (2) to continue to determine a new target from the remaining targets until the criterion is true or the candidate target set is empty. e implementation is shown in lines 8 to 23 in Algorithm 1.…”

“…WTA plays an increasing role given the growing requirement for the collaborative engagement of several weapons relative to several targets in modern battlefields [1]. Various extant studies focused on solutions for WTA that are mainly applied in the traditional high-value weapon platforms such as air defense WTA [2][3][4] or ship target strikes [5,6]. Compared to expensive equipment, swarming UAVs or UGVs attract the attention of military powers worldwide because of advantages including strong autonomy, low cost, high flexibility, and fast upgrades [7].…”

A Multiobjective Particle Swarm Optimization Algorithm Based on Multipopulation Coevolution for Weapon‐Target Assignment

“…According to the number of optimization objectives in the problem, WTA problems can be divided into single-objective WTA (SOWTA) problems, with only one optimization objective, and MOWTA problems, with at least two optimization objectives. Currently, research on the SOWTA problem is more in-depth, and respective optimization algorithms [3][4][5][6][7] can quickly obtain a better allocation scheme. For the SOWTA problem with the background of air defense intercept, Liu et al [4] proposed a firepower unit correlation matrix and designed a hybrid optimized algorithm based on the PSO and tabu search algorithm.…”

Ant Colony Optimization With Double Selections for Solving Integrated Scheduling Problem in Manufacturer

“…The other studies implementing ant colony optimization are ant colony optimization for permutation flowshop scheduling [3], ant colony optimization for job shop scheduling [4], applying ant colony optimization to increase the speed of the road network [5], ant colony optimization for solving the shortest path problem [6], ant colony optimization for generalized TSP problem [7], ant colony optimization for solving 3D TSP [8], and implementation of traveling salesman problem using ant colony optimization [9]. Meanwhile, the studies which improving ant colony optimization are solving traveling salesman problem based on ants with memory [10], modified ant colony optimization for grid scheduling [11], an improved ant colony optimization for machines scheduling [12], modified ant colony optimization by applying a local search procedure [13], Development of ant colony optimization based on statistical analysis and hypothesis testing [14], ant colony optimization with fault tolerance [15], a novel hybrid ant colony optimization [16], improved ant colony optimization forweapon-target assignment [17], ant colony clustering algorithm [18], improving ant colony optimization algorithm in transmission status rule [19].…”