Aiming at the typical problems of deploying sonar buoys in appointed sea area, this paper summarizes two problems existing in previous studies and puts forward a quick deployment method for sonar buoys detection under the overview situation of underwater cluster targets. Firstly, considering the influence of an underwater target course on target strength, the overlapping coefficient "buoy group" mode is introduced to deploy the array. And combining with the random distribution law of underwater targets in the exploration area, the mathematical optimization model for sonar buoys detection under the overview situation of underwater cluster targets is established. Then, the fitness function corresponding to the buoy deployment optimization model is defined, and the adaptive fireworks algorithm is used to solve the optimization problem for obtaining the sonar buoys deployment scheme. Finally, through the comparison and analysis of results for the seven group simulation experiments, the conclusions that are beneficial to improve the detection efficiency of the sonar buoy deployment are obtained. The proposed method can provide useful support for underwater cluster multi-target detection and the problem of counterattack underwater cluster multi-platform.

Aiming at the problem of using fixed-wing UAVs (clusters) for rapid coverage search of submarines in large-scale sea areas, the Strategy of using a single UAV for Coverage Searching in form of Dubins Curve(SCSDC) and the Strategy of using fixed-wing UAVs cluster for cooperative Coverage Searching in form of Parallel Intervals(SCSPI) were proposed after selective removal of concave points in complex irregular concave polygonal areas. Firstly, the cost mathematical model of cooperative search for multiple UAVs was established. At the same time, considering the limitation of turning radius of UAVs, a Dubins curve coverage search strategy was proposed to avoid search blind areas. Secondly, the judgment conditions of “edge length threshold” and “angle threshold” were introduced, and the method of selective removal of concave points was proposed to transform the irregular concave polygon area into a regular convex polygonal area. Then, in order to reduce the searching cost of UAV’s “Ω” turning type and improve the timeliness of its submarine search, a strategy of UAV cluster cooperative coverage search in the form of parallel interval was proposed. Finally, a comparative verification experiment was designed for the strategy of SCSDC and SCSPI in a complex irregular polygon search area. The simulation results show that selection of the two different area coverage search strategies needs to be determined according to actual combat requirements. The experimental results well verify the effectiveness and application scope of the two area coverage search strategies, and provide information support for subsequent anti-submarine action.

The decision of intelligent minefield attacking tank forces is a complex multi-constraint and multi-objective nonlinear optimization problem. Aiming at the common defects of commonly used intelligent algorithms and combining with the characteristics of fireworks algorithm, this paper proposed an adaptive fireworks algorithm to deal with it. In this paper, we first established the mathematical model of this problem and transformed the model into an unconstrained single-objective extremum by using the external penalty function method. Furthermore, the adaptive fireworks algorithm is used to solve the model. In order to verify the superiority of adaptive fireworks algorithm to deal with this problem, experimental results show that the adaptive fireworks algorithm has faster convergence speed and shorter computation time than the other algorithms, and the results can intuitively describe the reasonable task allocation scheme of the complex situation, which provides a foundation for studying the force control.

Because of the low convergence efficiency of the typical Vicsek model, a Vicsek with static summoning points (VSSP) algorithm based on the Vicsek model considering static summoning points is proposed. Firstly, the mathematical model of the individual movement total cost on each summoning point is established. Then the individual classification rule is designed according to the initial state of the cluster to obtain the sub-clusters guided by each summoning point. Finally, the summoning factor is introduced to modify the course angle updating formula of the Vicsek model. To verify the effectiveness of the proposed algorithm and study the effect of the cluster summoning factor on the convergence rate, three groups of simulation experiments under different summoning factors are designed in this paper. To verify the superiority of the VSSP algorithm, the performance of the VSSP algorithm is compared with the classic algorithm by designing the algorithm performance comparison verification experiment. The results show that the algorithm proposed in this paper has good convergence and course angle consistency. The summoning factor is the sensitive factor of cluster convergence. This algorithm can provide a reference for efficient cluster segmentation movement.