For the satellite constellation design problem, the computational complexity of regional coverage performance evaluation has posed great difficulty to classical Pareto dominance-based algorithms discovering the entire...
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For the satellite constellation design problem, the computational complexity of regional coverage performance evaluation has posed great difficulty to classical Pareto dominance-based algorithms discovering the entire Pareto front of the problem, while the decision makers are often interested in a limited part of it. In this paper, a preference-based manyobjective evolutionary algorithm, HMOEA-T, is utilized to solve this problem. The optimization includes two steps. The first step describes the preference information of decision maker by target region, while the second step focuses the search process on the preferred region and maintaining well convergence and diversity within the region. A visualization method is applied to intuitively analysis the performance of different methods handling the problem. Experimental results have shown the advantage of integrating preference information into the optimization process, and the comparative study with other state-of-the-art preferencebased methods(T-MOEA/D and T-NSGA-III) indicate that the proposed method can achieve competitive and better performance.
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