Multi-fidelity Bayesian optimization strategy applied to Overall Drone Design

Nowadays, drones can be developed for a wide range of use cases, from infrastructure monitoring to sea rescue, urban mobility or military purposes. Which drone design is best suited for a specific mission? To answer this question, we need to solve a constrained optimization problem based on a multi-disciplinary design model that takes the mission into account. The model generally being a computationally expensive numerical model whose gradients are not available all the time encourages us to consider a Bayesian optimization approach. Such strategy is well known to achieve a trade-off between exploitation and exploration in order to find interesting minimal area with a reduced number of function evaluations. A multi-fidelity approach can improve even more the computational efficiency of the Bayesian optimization strategy. In this work, we aim at designing a fixed-wing drone (fully electric) for long range surveillance mission. Two fidelity level electric drone models are developed. For a given mission requirement, the final battery state of charge is optimized with respect to drone design variables. Optimizations are performed on several missions using both a mono and a multi-fidelity Bayesian optimization strategy. The interest of using a multi-fidelity method for overall drone design has been assessed. The multi-fidelity super-efficient global optimization algorithm (MFSEGO) appeared to need less budget to reach convergence than the mono-fidelity algorithm and to be more robust to the initial design of experiments

Towards a multi-fidelity & multi-objective Bayesian optimization efficient algorithm

International audienceBlack-box optimization methods like Bayesian optimization are often employed in cases where the underlying objective functions and their gradient are complex, expensive to evaluate, or unavailable in closed form, making it difficult or impossible to use traditional optimization techniques. Fixed-wing drone design problems often face this kind of situations. Moreover in the literature multi-fidelity strategies allow to consistently reduce the optimization cost for mono-objective problems. The purpose of this paper is to propose a multi-fidelity Bayesian optimization method that suits to multi-objective problem solving. In this approach, low-fidelity and high-fidelity objective functions are used to build co-Kriging surrogate models which are then optimized using a Bayesian framework. By combining multiple fidelity levels and objectives, this approach efficiently explores the solution space and identifies the set of Pareto-optimal solutions. First, four analytical problems were solved to assess the methodology. The approach was then used to solve a more realistic problem involving the design of a fixedwing drone for a specific mission. Compared to the mono-fidelity strategy, the multi-fidelity one significantly improved optimization performance. On the drone test case, using a fixed budget, it allows to divide the inverted generational distance metric by 6.87 on average

Optimisation bayésienne multifidélité appliquée à la conception de drones

International audienceNowadays, drones can be developed for a wide range of use cases, from infrastructure monitoring to sea rescue, urban mobility or military purposes. Which drone design is best suited for a specific mission? To answer this question, we need to solve a constrained optimization problem based on a multidisciplinary design model that takes the mission into account. The model generally being a computationally expensive numerical model whose gradients are not available all the time encourages us to consider a Bayesian optimization approach. Such strategy is well known to achieve a trade-off between exploitation and exploration in order to find interesting minimal area with a reduced number of function evaluations. A multi-fidelity approach can improve even more the computational efficiency of the Bayesian optimization strategy. In this work, we aim at designing a fixed-wing drone (fully electric) for long range surveillance mission. Two fidelity level electric drone models are developed. For a given mission requirement, the final battery state of charge is optimized with respect to drone design variables. Optimizations are performed on several missions using both a mono and a multi-fidelity Bayesian optimization strategy. The interest of using a multi-fidelity method for overall drone design has been assessed. The multi-fidelity super-efficient global optimization algorithm (MFSEGO) appeared to need less budget to reach convergence than the mono-fidelity algorithm and to be more robust to the initial design of experiments

Optimisation Bayésienne multi-fidélité sous contraintes, application au design de drone.

Abstract. In aeronautics, the first design stages usually involve to solve a constrained multi-disciplinaryoptimization problem. The Bayesian optimization strategy is a way to solve such a complex system. Thisapproach requires to evaluate the objective function and the constraints quite a few times. Evaluationsare generally performed using numerical models that can be computationally expensive. To alleviate theoverall optimization cost variable information sources can be used to make the evaluations. Typicallywe are dealing with cheap low fidelity models to explore the design space and expensive high fidelitymodels for exploitation. In the following work, a mono-fidelity Bayesian optimization method and itsmulti-fidelity counterpart are compared on two analytical test cases and on an aerostructural dronedesign constrained optimization problem. The multi-fidelity strategy allows to divide the computationalcost by 1.3 compared to the mono-fidelity one on these test case

Actes de la conférence CAID 2021 (Conference on Artificial Intelligence for Defense)

6th European Cyber Week (6ème semaine européenne de la cybersécurité et de la cyberdéfense

Actes de la conférence CAID 2021 (Conference on Artificial Intelligence for Defense)

6th European Cyber Week (6ème semaine européenne de la cybersécurité et de la cyberdéfense

Actes de la conférence CAID 2021 (Conference on Artificial Intelligence for Defense)

6th European Cyber Week (6ème semaine européenne de la cybersécurité et de la cyberdéfense)International audienc

Actes de la conférence CAID 2021 (Conference on Artificial Intelligence for Defense)

6th European Cyber Week (6ème semaine européenne de la cybersécurité et de la cyberdéfense)International audienc

Actes de la conférence CAID 2021 (Conference on Artificial Intelligence for Defense)

6th European Cyber Week (6ème semaine européenne de la cybersécurité et de la cyberdéfense)International audienc