# A Novel Genetic Algorithm for the Synthetical Sensor-Weapon-Target Assignment Problem
## 5 Conclusions  
The STA and WTA problems have been widely concerned in the previous studies. However, few researches have focused on the S-WTA problem. In this paper, we proposed a novel genetic algorithm for the synthetical S-WTA problem. First, the frameworks of S-WTA are deeply analyzed. Then, a new model based on a novel framework of S-WTA is proposed. Furthermore, in order to solve the proposed model, a novel genetic algorithm, GA-SWTA, is presented. In the proposed GA-SWTA algorithm, a prior knowledge-based population initialization method is put forward to improve the efficiency of the convergence. Afterwards, two novel repair operations are designed to handle the constrains of the problem.  
Figure 14: Comparison results of GA-SWTA-r2, GA-SWTA-r3, and GA-SWTA with respect to the objective value.  
The performance of the GA-SWTA is analyzed in 25 test instances. Extensive experiments with two state-of-the-art heuristic algorithms and one GA-based algorithm demonstrate that our proposed algorithm outperforms other algorithms in most of the test instances. Furthermore, experiments on the proposed initialization method and random initialization method show that our proposed initialization method can improve the efficiency of the convergence. Moreover, the performance of the presented R-STA and R-WTA repair operations are detailed, which illustrate that these operations can effectively handle the constraints of the S-WTA problem as well as enhance the convergence of the algorithm.  
According to these experimental results, we can conclude that our proposed algorithm can effectively solve the S-WTA problem. Compared with the traditional heuristic algorithms and evolutionary algorithm, the proposed algorithm has better convergence performance, which means that it can achieve better S-WTA scheme. However, since this algorithm is based on the framework of evolutionary computation, it cannot be performed in a real-time environment. Therefore, this proposed algorithm can be used in a non real-time and high-precision environment.  
Although GA-SWTA can effectively solve the S-WTA problem in most of the test instances, its performance is worse than MRBCH in very large-scale problems. This is because the evolution operators used in this paper, which are the key factors to affect the convergence of GA-SWTA, are originally designed for the WTA problem, so they may lose their efficiency in solving S-WTA problems. Therefore, in our future work, we will focus on the design of evolution operators. We hypothesize that if the evolution operators are designed appropriately, the performance of the proposed algorithms in the very large-scale problems can be greatly enhanced.  
In this paper, as we mainly focus on the framework of the S-WTA problem and the design of the solving algorithm, the configuration of the sensor-weapon system in practical application environment is neglected. Due to the rapid technological development of sensors like the array and wide range sensor systems, the sensor signal data can greatly affect the performance of the target detection and weapon guidance. In addition, the damage effect of the weapon on the target mainly depends on the type of targets and duration of destroying targets. Therefore, in our future work, we will also focus on these important elements, which may truly reflect the actual situation of the S-WTA problem, to build a more reasonable model.  
Conceptualization, X.L. and D.Z.; Methodology, X.L. and Q.P.; Software, X.L. and Z.Y.; Data curation, X.L. and J.H.; Formal analysis, X.L and Q.P.; Validation, X.L.; Writing--Original draft, X.L.; Writing--Review & editing, X.L.  
This work was supported by the Fundamental Research Funds for the Central Universities under Grant 3102019 ZX016.  
The authors are very grateful to the two anonymous referees for their valuable and constructive comments, which improved the manuscript significantly.  
The authors declare no conflicts of interest.