Due to the complex constraints, many uncertain factors and critical real-time demand of path planning for multiple Unmanned Combat Aerial Vehicle (multi-UCAV), an Improved Artificial Bee Colony (I-ABC) algorithm was proposed to solve the model of path planning for multi-UCAV. First, the Voronoi diagram of battle field space was conceived to generate the optimal area of UCAV's paths. Then the chaotic searching algorithm was used to initialize the collection of paths, which was regarded as foods of ABC algorithm. With the limited data, the initial collection could search the optimal area of paths perfectly. Finally simulations of the multi-UCAV path planning under various threats were carried out. The simulation results verify that I-ABC can improve the diversity of nectar source and the convergence rate of algorithm, and it can increase the adaptability of dynamic battlefield and unexpected threats for UCAV.

Survivability is one of the technical indicators, which needed to consider in the highest priority, in the design and use of Unmanned Aerial Vehicle (UAV). According to the requirements of real-time display of situation and visual expression of analysis results in modern battlefield simulation, UAV survivability analysis system was developed with MapX component technology. First the primary frame and characteristics of MapX were introduced briefly. The function and composition of UAV survivability analysis system were discussed. And the module partition and program flow of system were given. Then the functions of war zone map display-roaming-zooming, combat scenario and display and process of survivability data with MapX technology in VC++ environment were analyzed in detail. Finally the simulation examples of survivability analysis were given. Simulation experiments indicate that the battlefield geographic environment of UAV is factually, vividly and dynamically displayed in the system. It is convenient to analyze the influence factors on survivability.