Approximate Solutions in Space Mission Design

Authors:
Oliver Schütze;Massimiliano Vasile;Carlos A. Coello Coello
Affiliations:
CINVESTAV-IPN, Computer Science Department, Mexico City, Mexico;Department of Aerospace Engineering, University of Glasgow, Glasgow, Scotland;CINVESTAV-IPN, Computer Science Department, Mexico City, Mexico
Venue:
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Year:
2008

Citing 5
Cited 2

Epsilon efficiency

Journal of Optimization Theory and Applications
Combining convergence and diversity in evolutionary multiobjective optimization

Evolutionary Computation
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)

Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Computing finite size representations of the set of approximate solutions of an MOP with stochastic search algorithms

Proceedings of the 10th annual conference on Genetic and evolutionary computation
A hybrid multiagent approach for global trajectory optimization

Journal of Global Optimization

Enhancing Decision Space Diversity in Evolutionary Multiobjective Algorithms

EMO '09 Proceedings of the 5th International Conference on Evolutionary Multi-Criterion Optimization
A variational approach to define robustness for parametric multiobjective optimization problems

Journal of Global Optimization

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we address multi-objective space mission design problems. We argue that it makes sense from the practical point of view to consider in addition to the `optimal' trajectories (in the Pareto sense) also approximate or nearly optimal solutions since this can lead to a significant larger variety for the decision maker. For this, we suggest a novel MOEA which is a modification of the well-known NSGA-II algorithm equipped with a recently proposed archiving strategy which aims for the storage of the set of approximate solution of a given MOP. Using this algorithm we will examine several space missions and demonstrate the benefit of the novel approach.