Quantum-inspired diffusion Monte Carlo optimization algorithm applied to space trajectories and attitude maneuvers

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Quantum-inspired diffusion Monte Carlo optimization algorithm applied to space trajectories and attitude maneuvers

De Grossi, Federico / Circi, Christian

Highlights • An algorithm for solving global optimization problems which takes inspiration from quantum mechanics is presented. • Applied to space trajectory optimization and compared with PSO and DE the algorithm shows better performance. • In the application to attitude maneuvers with the presence of constraints, it finds better solutions than PSO and DE.

Abstract In this work, an algorithm for solving optimization problems is proposed, inspired by a computational method employed in Quantum Mechanics: the Diffusion Monte Carlo method, commonly used for the computation of ground states of many-particle systems. The optimization problem is re-formulated as the problem of sampling the ground state wave function of a particle subject to a potential based on the function to be minimized. The algorithm is applied to problems in space trajectory and spacecraft attitude maneuvers optimization, the first application is to the problem of transfer between circular orbits, the results obtained are compared to the results from the literature, then it is applied to the problem of rendezvous with the asteroid Pallas, and finally to the optimization of an attitude maneuver in the presence of several constraints, and the obtained results are compared to two widely used methods: Particle Swarm Optimization and Differential Evolution algorithms. The algorithm is of simple implementation, and the numerical results show better performance than the comparison methods in the considered problems.