Conceptual space Systems Design using Meta-Heuristic Algorithms

Details Conceptual space Systems Design using Meta-Heuristic Algorithms Conceptual space Systems Design using Meta-Heuristic Algorithms

Jan 2002

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2002iaf..confe.910k&link_type=abstract

IAF abstracts, 34th COSPAR Scientific Assembly, The Second World Space Congress, held 10-19 October, 2002 in Houston, TX, USA.,

Computer Science

Performance

Scientific paper

easily and explicitly by new design-to-cost philosophy, "faster, better, cheaper" (fast-track, innovative, lower-cost, small-sat). The objective of the Space Systems Design has moved from maximizing space mission performance under weak time and cost constraints (almost regardless of cost) but with technology risk constraints, to maximizing mission goals under cost and schedule constraints but with prudent technology risk constraints, or maximizing space mission performance per unit cost. Within this mindset, Conceptual Space Systems Design models were formulated as constrained combinatorial optimization problems with estimated Total Mission Cost (TMC) as its objective function to be minimized and subsystems trade-offs as decision variables in its design space, using parametric estimating relationships (PERs) and cost estimating relationships (CERs).Here a constrained combinatorial optimized "solution" is defined as achieving the most favorable alternative for the system on the basis of the decision-making design criteria. Two non-traditional meta-heuristic optimization algorithms, Genetic Algorithms (GAs) and Simulated Annealing (SA), were used to solve the formulated combinatorial optimization model for the Conceptual Space Systems Design. GAs and SA were demonstrated on SAMPEX. The model simulation statistics show that the estimated TMCs obtained by GAs and SA are statistically equal and consistent. These statistics also show that Conceptual Space Systems Design Model can be used as a guidance tool to evaluate and validate space research proposals. Also, the non-traditional meta-heuristic constrained optimization techniques, GAs and SA, can be applied to all manner of space, civil or commercial design problems.

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