Ant Colony Optimization Approach for Satellite Broadcast Scheduling Problem

This paper handles an optimization problem related to Low Earth Orbit (LEO) artificial satellites which can have a range of missions, including scientific research, weather observation, military support, navigation, Earth imaging, and communications. A LEO is simplest and cheapest for satellite placement and it has the potential advantages of high bandwidth, low communication time lag, reduced power requirement and antennas and high-resolution images. Besides these advantages, there exists a challenge for operating satellites in LEO because they are not visible from a given point of Earth at all times. So, a large number of satellites are needed if the mission requires uninterrupted connectivity. It is also possible to offer discontinuous coverage using an LEO satellite capable of storing data received while passing over one part of Earth and transmitting it later while passing over another part. A LEO satellite system consists of a set of satellites and a set of ground terminals. A problem that should be solved for the operators is to find a valid broadcasting pattern between satellites and ground stations which satisfies some technical constraints and maximizes a profit function. This problem was defined as Satellite Broadcast's Scheduling (SBS) problem in the related literature and it is known to be an NP-complete problem. Nevertheless being a problem firstly published almost 50 years ago, the number of published studies on the problem is limited and the proposed solution approaches have been mainly based on neural networks and metaheuristics. This paper proposes an ant colony optimization metaheuristic approach for the SBS problem. The proposed approach is compared with previous works using benchmark problems reported in the literature. Results are evaluated to be promising.