In this paper, a multi-objective particle swarm optimization (MOPSO) procedure has been developed and applied in the field of aircraft requirement analysis. In order to identify useful set-up schemes for algorithm control parameters, the optimisation procedure has been preliminarily verified with test-case functions. Moreover, specific tools have been implemented to improve MOPSO effectiveness in finding Pareto front as wide and uniform as possible. The optimization procedure has been subsequently applied to the preliminary definition of a civil transport aircraft configuration. Both maximum takeoff weight and block time have been selected as objective functions to be minimized. At the end of optimization process, useful sensitivity curves, showing cruise speed requirement effects on aircraft main characteristics, have been obtained. Finally, a comparison with a similar task driven by a genetic algorithm has been performed in order to highlight some advantages offered by MOPSO procedure.

Multiobjective Particle Swarm Optimization technique as an effective tool for aircraft requirements analysis

BLASI, Luciano;IUSPA, Luigi
2007

Abstract

In this paper, a multi-objective particle swarm optimization (MOPSO) procedure has been developed and applied in the field of aircraft requirement analysis. In order to identify useful set-up schemes for algorithm control parameters, the optimisation procedure has been preliminarily verified with test-case functions. Moreover, specific tools have been implemented to improve MOPSO effectiveness in finding Pareto front as wide and uniform as possible. The optimization procedure has been subsequently applied to the preliminary definition of a civil transport aircraft configuration. Both maximum takeoff weight and block time have been selected as objective functions to be minimized. At the end of optimization process, useful sensitivity curves, showing cruise speed requirement effects on aircraft main characteristics, have been obtained. Finally, a comparison with a similar task driven by a genetic algorithm has been performed in order to highlight some advantages offered by MOPSO procedure.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11591/181073
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