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Multi-objective evolutionary algorithms for low-thrust . . . (2005)
| Citations: | 67 - 3 self |
BibTeX
@MISC{Lee05multi-objectiveevolutionary,
author = {Seungwon Lee and Paul von Allmen and Wolfgang Fink and A. E. Petropoulos and R. J. Terrile},
title = {Multi-objective evolutionary algorithms for low-thrust . . . },
year = {2005}
}
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Abstract
We address the problem of optimizing a spacecraft trajectory by using three different multi-objective evolutionary algorithms: i) Non-dominated sorting genetic algorithm, ii) Pareto-based ranking genetic algorithm, and iii) Strength Pareto genetic algorithm. The trajectory of interest is an orbit transfer around a central body when the spacecraft uses a low-thrust propulsion system. We use a Lyapunov feedback control law called the Q-law to create an eligible trajectory, while the Q-law control parameters are selected with the multi-objective algorithms. The optimization goal is to minimize flight time and consumed propellant mass simultaneously. The Pareto fronts (trade-off surface between flight time and propellant mass) produced by these algorithms are evaluated by means of two quantitative metrics: 1) size of the dominated space and 2) coverage of two Pareto fronts. With the two metrics, a hierarchy of algorithms emerged. The nondominated sorting genetic algorithm and the strength Pareto genetic algorithm are equally effective, and they outperform the Pareto-based ranking genetic algorithm.
Keyphrases
multi-objective evolutionary algorithm strength pareto genetic algorithm propellant mass flight time pareto front quantitative metric q-law control parameter different multi-objective evolutionary algorithm central body trade-off surface pareto-based ranking genetic algorithm nondominated sorting genetic algorithm orbit transfer optimization goal ranking genetic algorithm lyapunov feedback control law genetic algorithm multi-objective algorithm spacecraft trajectory dominated space eligible trajectory low-thrust propulsion system
