  Active-imaging agile earth observation satellite (AI-AEOS) is a new
generation agile earth observation satellite (AEOS). With renewed capabilities
in observation and active im-aging, AI-AEOS improves upon the observation
capabilities of AEOS and provide additional ways to observe ground targets.
This however makes the observation scheduling problem for these agile earth
observation satellite more complex, especially when considering multi-strip
ground targets. In this paper, we investigate the multi-strip observation
scheduling problem for an active-image agile earth observation satellite
(MOSP). A bi-objective optimization model is presented for MOSP along with an
adaptive bi-objective memetic algorithm which integrates the combined power of
an adaptive large neighborhood search algorithm (ALNS) and a nondominated
sorting genetic algorithm II (NSGA-II). Results of extensive computa-tional
experiments are presented which disclose that ALNS and NSGA-II when worked in
unison produced superior outcomes. Our model is more versatile than existing
models and provide enhanced capabilities in applied problem solving.
