## 5 Results
### Simulation Results of the DWTA Problem
It can be seen that the set of non-dominated solutions obtained by IMOPSO for solving large-scale multiobjective DWTA problem can fully converge to the Pareto front, and most of the non-dominated solutions have the same values as the solutions of true PF. Some solutions generated by NSGA-II can converge to the true PF, but there is still a distance from the Pareto front in the middle location. The solution set obtained by MOPSO fails to converge to the true PF, but the distribution of solutions is relatively good. MOEA/D only finds three solutions. It can be seen that with the increase of DWTA problem scale, MOEA/D is getting worse and worse. DWTA problems, thus obtaining an effective weapon-target allocation scheme. The results of solving the multiobjective DWTA problem reflect the ability of IMOPSO in dealing with practical MOPs, which shows that IMOPSO algorithm is more suitable for solving large-scale mixed integer programming problems than other algorithms. We use the average value of each performance metric in the experiment to measure the improvement degree of IMOPSO compared with other algorithms of dealing with MOPs. The specific method is as follows: We sum the metric average values of each benchmark function, and then find the average value as the comprehensive index value. The DWTA problem also adopts the same processing method. Through the calculation, we can conclude that IMOPSO is superior to MOPSO, NSGA-II and MOEA/D in GD/\(\Delta\)/DW/IGD index, which are 99.61%/74.46%/99.68%/42.99%, 29.97%/39.90%/87.67%/22.50%, 35.33%/57.22%/96.96%/93.04%, respectively. It can be seen from the above values that the improved effect of the proposed IMOPSO algorithm on the basis of MOPSO algorithm is particularly remarkable, and the performance of IMOPSO in solving MOPs is also significantly ahead of NSGA-II and MOEA/D. IMOPSO can be regarded as the best of the four algorithms, which successfully overcomes the shortcoming that MOPSO is easy to fall into local optimum, and can also balance the contradiction between exploration and exploitation. Experimental results show that compared with other algorithms, IMOPSO is not only more robust and accurate, but also has a wide range of applications, which is more suitable for dealing with complex MOPs. Although the IMOPSO algorithm has many excellent performances, it is not completely flawless when dealing with MOPs. At first, the number of times that the IMOPSO algorithm needs to call the objective function in each iteration is very large. Since the benchmark functions have simple and clear mathematical expressions, it is not an insurmountable problem for the optimization system itself to call the objective function more times. However, many problems that need to be solved in engineering application are \"black box\" types, which cannot be directly expressed by mathematical formulas. This requires huge computer resources as support, so it is necessary to properly handle them in the optimization program. Secondly, in this paper, it is difficult to determine the specific number of unimproved generations when the \(g_{best}\) proceeds mutation operation, so we conducted many experiments to determine it.