Space Missions Engineering Laboratory

• Astrodynamics
• Space Robotics
• On-Board Autonomy
• Optimization
  • Multiobjective Optimization
  • Multidisciplinary Optimization
  • Global Optimization
  • Local Optimization
  • Heuristic Methods
• Soft Computing
• Autonomous Navigation

Global Optimization

In the last two decades, global optimization approaches have been extensively used towards the solution of the complex problem of space mission design. As operational costs have been increasingly reduced, space systems engineers have been facing the challenging task of maximizing the payload-launch mass ratio while still achieving the primary mission goals. Consequently, nowadays the aim of the space mission design is to find the best solution in terms of propellant consumption under the constraint of achieving the mission goals, instead of finding a general solution to the problem. In the formalism of optimization, this means that the problem consists in looking for the best design solution in the regions of the search space satisfying the problem constraints. Unfortunately, the problem of space mission design is characterized by objective functions having a large number of local minima. This causes classical local gradient-based optimization methods to usually converge to one of these local minima.

Then, universities and aerospace industries are currently focusing research activities on the identification of advanced algorithms to solve space related global optimization problems. The task is not trivial due to the occurrence of several difficulties. Most optimization problems to be faced show mixed combinatorial-continuous features that are unlikely solvable by means of standard algorithms. Moreover, despite the increasing effectiveness of state-of-art algorithms, associated optimization processes still require high computational effort. Finally, the efficiency of every algorithm, both computational and performance-wise, is strongly linked to the type of problem that has to be solved.

Several years of relevant experience in the global optimization field characterize our group. The department has been contributing to the study and development of innovative tools based on the most promising classes of methods. Great experience has been gained in the field of stochastic methods with the development of algorithms based on Genetic Algorithms, Evolutionary Programming and Evolutionary Strategies. Moreover, promising results have been recently obtained in the field of deterministic methods, which are encouraging the possibility of developing new metamodel-based optimization tools.