Enhancing the search ability of a hybrid LSHADE for global optimization of interplanetary trajectory design

The global optimization of interplanetary trajectory design is a challenge and tough problem in deep space. To cope with the extreme nonlinearity of the search space and a large number of locally optimal solutions, a novel hybrid algorithm based on success-history based adaptive differential evolution with linear population size reduction (LSHADE), called HLSHADE, is proposed. In HLSHADE, after the global search finds a better solution, a new two-step local search with an adaptive control parameters strategy is designed to enhance the exploitation ability of HLSHADE. The presented method is tested on well-known global trajectory optimization problems (GTOPs) developed by the Advanced Concepts Team of the European Space Agency (ESA). Experimental results have demonstrated the competitive performance of HLSHADE with respect to the three sets of compared algorithms, including LSHADE and ten of its variants, six famous algorithms in the IEEE Competitions on Evolutionary Computation (CEC) and eleven algorithms in the software platform PyGMO developed by the ESA's Advanced Concepts Team.

Automated summary

A novel hybrid algorithm based on adaptive differential evolution is proposed for the global optimization of interplanetary trajectory design. Experimental results demonstrate its competitive performance.