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After 30 years of success of Ariane launches, Astrium Space Transportation as primecontractor is preparing the future of launch vehicles with research and development activities. This paper describes the results of the collaboration between INRIA and Astrium to solve the typical multidisciplinary problem of expendable launch vehicle design thanks to the Covariance Matrix Adaptation Evolution Strategy (CMA-ES). The different disciplines integrated in the Multidisciplinary platform are propulsion system, aerodynamics, mass budget, trajectory integration, control. CMA-ES was tested on a two-liquid-staged launcher with solid boosters. The algorithm produced conclusive results on an optimization problem that proved to be very ill-conditioned. The comparison with Non-Dominated Sorting Genetic Algorithm NSGA-II gave equivalent results on a bi-level optimization, the trajectory subproblem being solved separately by a reduced gradient method. The good performance of CMA-ES on a single launcher case allowed us to extend the tests on a launcher family. A launcher family is composed of several launcher configurations sharing common characteristics with different payload targets and optimized together. In these last cases, CMA-ES surpasses NSGA-II in terms of performance and was able to handle multiple error cases during the search of optimum. 2 ΑοΑ = Angle-of-Attack m = mass Q = mass flow rate T = Thrust Is = Specific Impulse X = launcher position vector V = launcher velocity vector V = V modulus Vr = launcher relative velocity vector (absolute velocity minus Earth training velocity) Vr = Vr modulus γ = launcher acceleration vector g = gravity acceleration vector g0 = 9.80665 m/s² θ = launcher pitch orientation in a given galilean frame ψ = launcher yaw orientation in a given galilean frame ρ = density CD = drag coefficient Sref = Reference area Pdyn = Dynamic Pressure (0.5ρVr²) Φ = Heat flux (0.5ρVr 3 ) c = chord f, g = generic functions h = height

Abstract-Landing on distant planets is always a challenging task due to the distance and hostile environments found. In the design of autonomous hazard avoidance systems we find the particularly relevant task of landing site selection, that has to operate in real-time as the lander approaches the planet's surface. Seeking to improve the computational complexity of previous approaches to this problem, we propose the use of non-exhaustive search methodologies. A comparative study of several algorithms, such as Tabu Search and Particle Swarm Optimization, was performed. The results are very promising, with Particle Swarm Optimization showing the capacity to consistently produce solutions of very high quality, on distinct landing scenarios.

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