Preference-based Evolutionary Many-objective Optimization for Regional Coverage Satellite Constellation Design

DOI

10.2991/mbdasm-19.2019.3How to use a DOI?

Keywords

evolutoinary algorithm; preference articulation; constellation design; many-objective optimization

Abstract

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 many-objective 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 preference-based methods (T-MOEA/D and T-NSGA-III) indicate that the proposed method can achieve competitive and better performance.

Copyright

© 2019, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Minghui Xiong
AU  - Wei Xiong
PY  - 2019/10
DA  - 2019/10
TI  - Preference-based Evolutionary Many-objective Optimization for Regional Coverage Satellite Constellation Design
BT  - Proceedings of the 2019 International Conference on Mathematics, Big Data Analysis and Simulation and Modelling (MBDASM 2019)
PB  - Atlantis Press
SP  - 8
EP  - 14
SN  - 2352-538X
UR  - https://doi.org/10.2991/mbdasm-19.2019.3
DO  - 10.2991/mbdasm-19.2019.3
ID  - Xiong2019/10
ER  -