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The size and complexity of communication satellite payloads have been increasing very quickly over the last years and their configuration / reconfiguration have become very difficult problems. In this work, we propose to compare the efficiency of three well-known metaheuristic methods to solve an initial configuration problem, which objective is to minimise the length of the longest channel path. Experiments are conducted on real-world problem instances with realistic operational constraints (e.g., a maximum computation time of 10 minutes) and Wilcoxon test is used to determine with statistical confidence what technique is more suitable and what are its limitations. The results of this work will serve as an initial step in our research to design hybrid approaches to push even further the solving capabilities, i.e., tackling bigger payloads and more channels to activate.

Abstract. The increasing complexity of current telecommunication satellite payloads has made their manual management a difficult and error prone task. As a consequence, efficient optimisation techniques are required to help engineers to configure the payload. Recent works focusing on exact approaches faced scalability issues while metaheuristics provided unsatisfactory solution quality. This work therefore proposes three hybridisation schemes that combine both metaheuristics and an exact method. We focus on the initial configuration problem case and we consider as objective to minimise the length of the longest channel path. Experimental results on realistic payload sizes demonstrate the advantage of those approaches in terms of efficiency within a strict operational time constraint of ten minutes on a single CPU core.

No abstract

Today's world is a vast network of global communications systems in which satellites provide high-performance and long distance communications. Satellites are able to forward signals after amplification to offer a high level of service to customers. These signals are composed of many different channel frequencies continuously carrying real-time data feeds. Nevertheless, the increasing demands of the market force satellite operators to develop efficient approaches to manage satellite configurations, in which power transmission is one crucial criterion. Not only the signal power sent to the satellite needs to be optimal to avoid large costs but also the power of the downlink signal has to be strong enough to ensure the quality of service. In this work, we tackle for the first time the bi-objective input/output power problem with multi-objective evolutionary algorithms to discover efficient solutions. A problem specific indirect encoding is proposed and the performance of three state-of-the-art multiobjective evolutionary algorithms, i.e. NSGA-II, SPEA2 and MOCell, is compared on real satellite payload instances.