Deep-space trajectory optimizations using differential evolution with self-learning

This paper presents spacecraft trajectory optimizations for deep-space missions requiring multiple gravity-assists (MGA). The main algorithm is based on a self-adaptive/self-learning differential evolution (DE). In the process of improving the performance of DE for optimizing the MGA trajectory, the proposed algorithm alleviates the dependence on predetermined mutation strategy and control parameters in DE; as evolution progresses, the mutation strategy and the control parameters switch adaptively to more promising ones by reflecting experiences in previous evolution steps. Furthermore, the proposed algorithm is equipped with a re-initialization technique to directly mollify the issue of converging to a local optimum, which is often observed when optimizing the MGA trajectory. In order to demonstrate these favorable characteristics, the proposed algorithm is implemented to solve six well-known MGA trajectory optimization problems designed by the European space agency (ESA). Compared with the latest representative evolutionary algorithms, the proposed algorithm can successfully converge to the currently known best solutions of five MGA problems; our solutions to four of those MGA problems are better than currently known solutions. The proposed algorithm also performs well as a local/auxiliary search algorithm to improve the performance of other evolutionary algorithms. In addition to describing the algorithms and solutions characteristics, sensitivity analysis is presented to quantitatively investigate the search capability of finding the optimal solutions of MGA problems. The overall results show that our self-learning DE is competitively compared with other representative algorithms in terms of convergences to the global optimum, reliable search capability, and applicability to a variety of deep-space trajectory optimizations. •Propose a new self-learning/adaptive differential evolution algorithm for optimizing complex deep-space trajectories.•The proposed algorithm adaptively selects suitable mutation strategies and parameter values.•The proposed algorithm is also shown to be effective as a local/auxiliary optimizer.•Sensitivity analysis shows some distinctive characteristics of the proposed algorithm.