Abstract
Some types of space trajectory design problems present highly multimodal, globally non-convex objective functions with a large number of local minima, often nested. This paper proposes some memetic strategies to improve the performance of the basic heuristic of differential evolution when applied to the solution of global trajectory optimisation. In particular, it is often more useful to find families of good solutions rather than a single, globally optimal one. A rigorous testing procedure is introduced to measure the performance of a global optimisation algorithm. The memetic strategies are tested on a standard set of difficult trajectory optimisation problems.
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Vasile, M., Minisci, E. (2009). Memetic Strategies for Global Trajectory Optimisation. In: Cai, Z., Li, Z., Kang, Z., Liu, Y. (eds) Advances in Computation and Intelligence. ISICA 2009. Lecture Notes in Computer Science, vol 5821. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04843-2_20
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DOI: https://doi.org/10.1007/978-3-642-04843-2_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04842-5
Online ISBN: 978-3-642-04843-2
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