The problem of optimal design of a multi-gravity-assist space trajectory, with free number of deep space maneuvers (MGADSM), poses a multi-modal cost function. In the general form of the problem, the number of design variables is solution dependent. This paper presents a comparison between two recently developed genetic-based methods to handle global optimization problems where the number of design variables vary from one solution to another. The first method is the hidden genes genetic algorithms (HGGA) and the second method is the dynamic-size multiple populations genetic algorithms (DSMPGA). In this paper, both methods are used to find solutions for the Jupiter Europa Orbiter mission.
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