###### Abstract
Generally, the evaluation index of WTA problem adopted is the overall damage probability of the target. An excellent WTA scheme should not only achieve the purpose of combat, but also minimize the cost of combat. Because the smaller the ammunition cost and the better the combat effect are two conflicting goals, the actual WTA is a multiobjective optimization problem (MOP). Although some algorithms are weighted multiple evaluation indexes into one cost function, these algorithms still aim to optimize a single objective. The defect of this method is that the weight of the objective is usually generated through experience, and improper weight selection will seriously affect the optimization effect of the algorithm and may produce a decision scheme that the commander does not really expect. Many multiobjective optimization techniques have been studied to solve multiobjective WTA optimization problems over the last decade. [PERSON] et al. [18] solved the target-based multiobjective optimization DWTA problem by using NSGA-II. [PERSON] et al. [19] proposed an adaptive mechanism to solve the multi-stage weapon target allocation problem and compared the adaptive NSGA-II and adaptive MOEA/D with other algorithms [20]. [PERSON] et al. [21] proposed an improved Pareto ant colony optimization method to solve the target-based multiobjective SWTA optimization problem. All these researches show the advantage of solving WTA with multiobjective optimization algorithm. The purpose of this paper is to use multiobjective optimization algorithm to solve DWTA problem. At present, the well-known multiobjective optimization algorithms are SPEA-II [22], PESA-II [23], NSGA-II [24], MOEA/D [25], MOPSO [26], etc. Among these algorithms, MOPSO is simple in principle and easy to implement, so it is used to solve multiobjective optimization problems in engineering application. The MOPSO algorithm was proposed by [PERSON] et al. in the early 21 st century. Since then, many scholars have done a lot of research on MOPSO and put forward many excellent MOPSO algorithms [27; 28; 29; 30; 31]. The purpose of these algorithms is to obtain more accurate Pareto front with good distribution characteristics when solving MOPs. MOPSO has many advantages that traditional optimization algorithms cannot achieve. However, many researchers find that MOPSO has some defects in solving problems of engineering applications, the most notable of which is that MOPSO is easy to converge in advance, leading to falling into local optimal solution. In accordance with the characteristics of DWTA and the weakness of MOPSO in solving DWTA problems, we propose an improved multiobjective particle swarm optimization algorithm (IMOPSO). The main contributions of the algorithm are described as follows: (1) A novel particle learning strategy is proposed. According to the evolutionary characteristics of dominated and non-dominated solutions in MOPSO, various learning strategies are adopted, which makes the algorithm can learn and evolve in a targeted way. (2) A novel search strategy based on simulated binary crossover and polynomial mutation is proposed, which enables elitist information to be shared among external archive and enhances the exploratory capabilities of IMOPSO. (3) A dynamic external archive maintenance strategy is proposed. Compared with earlier archive maintenance methods, a dynamic external archive maintenance strategy can improve the diversity of non-dominated solutions. The rest of this paper is organized as follows. In Section 2, some basic definitions of the MOP, the DWTA problem and its mathematical model are introduced and analysed. In Section 3, MOPSO and the improved method are described. In Section 4, the experimental research is introduced. In Section 5, the experimental results are analyzed. The experimental results are discussed in Section 6. The conclusion and future work are described in Section 7.