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Multidisciplinary design of a guided flying vehicle using simplex nondominated sorting genetic algorithm II

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Abstract

This paper presents design of a typical Guided Flying Vehicle (GFV) using the multidisciplinary design optimization (MDO). The main objectives of this multi-disciplinary design are maximizing the payload’s weight as well as minimizing the miss distance. The main disciplines considered for this design include aerodynamics, dynamic, guidance, control, structure, weight and balance. This design of GFV is applied to three and six Degree of Freedom (DOF) to show comparison of simulation results. The hybrid scheme of optimization algorithm is based on Nelder-Mead Simplex optimization algorithm and Nondominated Sorting Genetic Algorithm II (NSGA II), called Simplex-NSGA II. This scheme is implemented for finding an optimal solution through the MDO. The Simplex-NSGA II method is a heuristic optimization algorithm that applies to multi-objective functions and the results are then compared with the most famous algorithms, like Nondominated Sorting Genetic Algorithm II (NSGA II) and Multi-Objective Particle Swarm Optimization (MOPSO). Simulation results demonstrate the superior performance of the Simplex-NSGA II over NSGA II and MOPSO. Also, it is used in this study in order to achieve an optimal solution using MDO in both 3DOF and 6DOF simulations of GFV to reach desirable performance index.

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Authors and Affiliations

  1. School of Information Technologies, University of Sydney, Sydney, Australia

    Seid Miad Zandavi

  2. Department of Aerospace Engineering, Sharif University of Technology, Tehran, Iran

    Seid H. Pourtakdoust

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  1. Seid Miad Zandavi
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  2. Seid H. Pourtakdoust
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Correspondence to Seid Miad Zandavi.

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Zandavi, S.M., Pourtakdoust, S.H. Multidisciplinary design of a guided flying vehicle using simplex nondominated sorting genetic algorithm II. Struct Multidisc Optim 57, 705–720 (2018). https://doi.org/10.1007/s00158-017-1776-3

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  • DOI: https://doi.org/10.1007/s00158-017-1776-3

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