A case learning-based differential evolution algorithm for global optimization of interplanetary trajectory design

Abstract The problem of optimally designing an interplanetary trajectory for a space mission is considered in this paper. To tackle the extreme non-linearity of the search space, a case learning-based differential evolution algorithm, named CLDE, is proposed. It stores successful control parameters (scaling factor and crossover possibility) and retrieve the available reference information according to a geographic similarity in each generation. To depart from the basin of attraction of a local optimum, CLDE will give up learning from the successful cases once no better offsprings have been obtained within a certain number of generations and generate new control parameters. Two versions of CLDE have been developed, for global optimization (G-CLDE) and local optimization (L-CLDE), respectively. Their performance has been tested on GTOP benchmarks and real mission design. Experimental results show that G-CLDE performs better than related algorithms, including PYGMO algorithms and recently published L-SHADE variants. L-CLDE can improve upon the best known solution for the Messenger benchmark (full version). By connecting G-CLDE and L-CLDE together, CLDE finds promising results in acceptable computational time on the GTOP benchmark.