MHACO: a multi-objective hypervolume-based ant colony optimizer for apace trajectory optimization

Giacomo Acciarini, Dario Izzo, Erwin Mooij

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In this paper, we combine the concepts of hypervolume, ant colony optimization and nondominated sorting to develop a novel multi-objective ant colony optimizer for global space trajectory optimization. In particular, this algorithm is first tested on three space trajectory bi-objective test problems: an Earth-Mars transfer, an Earth-Venus transfer and a bi-objective version of the Jupiter Icy Moons Explorer mission (the first large-class mission of the European Space Agency’s Cosmic Vision 2015-2025 programme). Finally, the algorithm is applied to a four-objectives low-thrust problem that describes the journey of a solar sail towards a polar orbit around the Sun. The results on both the test cases and the more complex problem are reported by comparing the novel algorithm performances with those of two popular multi-objective optimizers (i.e., a nondominated sorting genetic algorithm and a multi-objective evolutionary algorithm with decomposition) in terms of hypervolume metric. The numerical results of this study show that the multi-objective hypervolume-based ant colony optimization algorithm is not only competitive with the standard multi-objective algorithms when applied to the space trajectory test cases, but it can also provide better Pareto fronts in terms of hypervolume values when applied to the complex solar sailing mission.
Original languageEnglish
Number of pages8
Publication statusPublished - 20 Jul 2020
EventIEEE World Congress on Computational Intelligence (WCCI) 2020 - Online, Glasgow, United Kingdom
Duration: 19 Jul 202024 Jul 2020


ConferenceIEEE World Congress on Computational Intelligence (WCCI) 2020
Abbreviated titleIEEE WCCI2020
Country/TerritoryUnited Kingdom
Internet address


  • global optimization
  • space trajectory optimization problems
  • ant colony optimization (ACO)
  • hypervolume


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