## 1 Introduction  
Along with the rapid development of information technologies, such as embedded systems, Artificial Intelligence, Internet of Things, Cloud computing and so on [1; 2], diverse intelligent systems and application domains have been emerging increasingly, in particular, Connected Intelligent Vehicles (CIVs) and Intelligent Transportation Systems (ITS), Unmanned Aerial Vehicles (UAVs) and combat cloud, etc. More recently, such intelligent systems have been presenting amazing intelligent capabilities. As an aid to individual intelligence, varied communication technologies, such as 5G and data link, have made such systems more and more networked. In addition, these technologies made it possible to construct new Swarm Intelligent Systems (SIW), which operate with a decentralized, collective and self-organized mode, and are always service-oriented [3; 4]. This is one important coming trend. Such systems will be stronger because the deep connection of intelligent systems allow coordinating and cooperating through the whole system, from different levels and using multiple aspects. Typically, there are three cooperation problems that concern researchers: the behavior-level cooperation (e.g., between CIVs [5; 6; 7] or Numerical Control Machines [8]), the computation-level cooperation (e.g., within a satellite fleet or edge clouds [9]), and the mission-level cooperation (e.g., within a fleet of robots or flights [10; 11]). Obviously, mission-level cooperation should be the most complicated of these three aforementioned problems, and as we know, the mission-level cooperation for robot fleet has been a concern and has been studied in recent decades. Its main connotation refers to the fact that a specific mission will be accomplished through a group of smart objects automatically and cooperatively, every member of which achieve dedicated collective actions. In fact, this kind of cooperation is always restricted by several different factors, covering the constraints of time, location, distance, energy, payloads and so on. For instance, There are several typical scenes. Automatic machines within an automating production line collaborate with each other to finish the product processing [12]. Of course, this kind of cooperation between these machines is mainly down to rhythmical coordination, without considering dynamical constraints. However, the cooperation of mobile robots within a fleet, typically UAV fleet, is often more different and complex. This is because to accomplish one mission collectively, the cooperation procedure will be closely related to several important aspects, for instance, allocating sub-missions rationally to fleet members, the behavior cooperation, capabilities of the fleet members, open environment, and other cyber-physical factors [13; 14; 15; 16; 17]. Today, along with the rapid development of embedded hardware/software and network technologies, such problems have been becoming more complicated for some complex applications of swarm robots that come with several constraints, e.g., combat cloud. Beyond performing sub-missions synergistically, such swarm applications always need to deeply cooperate with each other via sharing their local resources. The advantage of this manner is significant. Through more fine-grained scheduling and reconfiguring of various resources of swarm systems, it will promote the reliability, robustness and flexibility of swarm cooperation as well as the performance. Essentially, it has evolved to be one problem with more constraint dimensions than that of traditional mission cooperation, and its main thought is also somewhat similar to that of cloud computing. After considering these new features and the challenges to use heterogeneous resources cooperatively and efficiently, in this paper, optimized planning models and methods between sub-missions and resources for swarm systems are mainly studied.