Simplified swarm optimization with initialization scheme for dynamic weapon–target​ assignment problem

Efficient Decision Approaches for Asset-Based Dynamic Weapon Target Assignment by a Receding Horizon and Marginal Return Heuristic

“…The operation process of weapon selection based on statistical marginal return is as follows: assuming that the priority defense asset t p and the priority attack target t p are generated by Equation (25), and the marginal return matrix E = [e ij ] m×n of "weapon-target-priority asset" is generated by Equation (31). The heuristic information G = {g 1 , g 2 , .…”

“…Wang [30] designed a discrete particle swarm algorithm based on the intuitionistic fuzzy entropy. Lai and Wu [31] proposed an improved simplified swarm optimization with deterministic initialization and target exchange scheme. However, the population-based random search mechanism lacks rigorous theoretical proofs in terms of convergence.…”

Research on Dynamic Weapon Target Assignment Based on Cross-Entropy

“…Weapon target assignment (WTA) problem is the core content in the research of combat command aided decisionmaking, which can be categorized as a combinatorial optimization problem and nondeterministic polynomial complete problem [1], whose solution space expands exponentially with the increasing of the number of weapons and targets. Generally, WTA problem can be divided into static weapon target assignment (SWTA) problem [2,3] and dynamic weapon target assignment (DWTA) problem [4][5][6]. In SWTA, all weapons are assigned to targets simultaneously and all information is known; however, in DWTA, many dynamic changes such as time window and weapon consumption should be considered; in that case, the solving algorithm for DWTA problem must have the good real-time performance.…”

“…Zhou et al [3] proposed a discrete particle swarm optimization (PSO) algorithm to maximize the ratio of global operation effectiveness to the costs of consumed weapons. Lai and Wu [4] proposed an improved simplified swarm optimization method with two novel schemes to minimize the threat value in the multistage WTA. Chang et al [5] proposed an artificial bee colony algorithm with heuristic factor initialization to get the minimal expected value of surviving targets for all stages.…”

“…With the development of computer performance, numerous methods are developed to solve the WTA problems, such as branch and bound [2], PSO [3,8,13], artificial bee colony algorithm [5], genetic algorithm [10][11][12], large-scale neighborhood algorithm [14,15], and ant colony algorithm [16,17]. Some new methods are also applied to deal with the WTA problem such as simplified swarm optimization [4], multiscale quantum harmonic oscillator algorithm [7], marginal-return-based constructive heuristic [9], and Markov decision model [18]. Some scholars are also interested in hybrid algorithms [19][20][21].…”