## 1 Introduction  
As an important part of current and next generation networks, Internet of Things (IoT) is composed of physical devices, vehicles, home appliances, and other items embedded with electronics, software, sensors, actuators, and seamless connectivity, which enables these things to connect and exchange data [1; 2; 3; 4; 5; 6; 7; 8; 9]. In particular, IoT is deployed with satellite channels in geographically remote locations for agriculture, mining and transportation [10], and optimized data exchange in heterogeneous IoT architectures allows increasing data transmission efficiency and extending the application areas for the IoT technologies. Therefore, bringing wide-area connectivity to the IoT using spatial nodes and satellite technology is becoming an attractive solution to complementing terrestrial networks [11; 12; 13; 14]. To provide a robust solution for the NP-hard problem, Computational Intelligence (CI) solves many optimization problems in real-world through biologically-and-linguistically-inspired techniques [15; 16; 17]. However, for different scenarios, the CI algorithm needs to be modified to be applicable [18; 19; 20]. Particularly, for data transmission in the spatial node-based IoT with limited resource, it is likely to affect the system performance seriously if the classical CI algorithm is applied bluntly [21; 22]. In order to cope with intermittent connectivity caused by the orbital motion of spatial nodes, the spatial node-based IoT networks are often modeled through scheduling contacts as a temporal topology within a certain duration [23]. For the spatial nodes with limited on-board resources, the energy budget and the number of transponders make the node unable to fully utilize all the potential contacts, and there will be a conflict in contact establishment between the nodes. Contact Plan Design (CPD), which was proposed to resolve conflicts for the contact scheduling in the spatial resource-constrained IoT networks, is attracting more and more attention [24; 25]. Early works mainly focused on the reliability of connections between nodes, while ignoring the time-varying nature of the topology caused by node movement [26; 27]. Considering the dynamic characteristic of network topology, the space-time graph was proposed to discretize the time-varying network topology, and a sparser topology was generated from the original topology so as to reduce the total network overhead in [28; 29]. To minimize the network overhead when contacts are unreliable, different reliabilities were allocated to each contact and several heuristic methods were proposed in [30]. Taking account of the limited-resources on satellites, authors in [31] proposed Fair Contact Plan (FCP) to enable fair scheduling of contacts based on the historical establishing times of each contact. To improve the transfer efficiency with low computational complexity, metaheuristics were introduced to CPD [32; 33]. A Time-Evolving Graph (TEG) was introduced to characterize the data acquisition and delivery process while maximizing delay-constrained throughput in [34]. In view of the dynamic change of contact capacity, authors in [35] treated CPD as a queue-stability related stochastic optimization problem, and designed the Contact Plan (CP) in a slot-by-slot manner. Unfortunately, almost all of aforementioned CPD schemes were proposed for the resource-constrained satellite networks, which could hardly meet the requirements of massive and timely data transmission. The intelligent networking with different space platforms, which aims to achieve continuous and low-delay transmission of massive data, is becoming an attractive and hot research field. In the scenario of intelligent networking, more low-altitude nodes such as Unmanned Aerial Vehicles (UAVs) and High Altitude Platforms (HAPs) are introduced to perform collaborative communications with traditional satellite nodes [36; 37].