Multi-objective evolutionary approach for the satellite payload power optimization problem

Abstract

International audienceToday’s world is a vast network of global communicationssystems in which satellites provide high-performanceand long distance communications. Satellites are able to forwardsignals after amplification to offer a high level of serviceto customers. These signals are composed of many differentchannel frequencies continuously carrying real-time data feeds.Nevertheless, the increasing demands of the market force satelliteoperators 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 optimalto avoid large costs but also the power of the downlink signalhas to be strong enough to ensure the quality of service. In thiswork, we tackle for the first time the bi-objective input/outputpower problem with multi-objective evolutionary algorithms todiscover efficient solutions. A problem specific indirect encodingis proposed and the performance of three state-of-the-art multiobjectiveevolutionary algorithms, i.e. NSGA-II, SPEA2 andMOCell, is compared on real satellite payload instances