Multiobjective meta-heuristic with iterative parameter distribution estimation for aeroelastic design of an aircraft wing

This paper proposes a new self-adaptive meta-heuristic (MH) algorithm for multiobjective optimisation. The adaptation is accomplished by means of estimation of distribution. The differential evolution reproduction strategy is modified and used in this dominance-based multiobjective optimiser whereas...

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Vydáno v:Engineering with computers Ročník 38; číslo 1; s. 695 - 713
Hlavní autoři: Wansasueb, Kittinan, Pholdee, Nantiwat, Panagant, Natee, Bureerat, Sujin
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Springer London 01.02.2022
Springer Nature B.V
Témata:
Adaptive algorithms
Aeroelasticity
Aircraft
Aircraft design
Algorithms
CAE) and Design
Calculus of Variations and Optimal Control; Optimization
Classical Mechanics
Computer Science
Computer-Aided Engineering (CAD
Control
Evolutionary computation
Flutter
Heuristic
Heuristic methods
Math. Applications in Chemistry
Mathematical and Computational Engineering
Multiple objective analysis
Optimization
Original Article
Parameter estimation
Ply orientation
Structural weight
Systems Theory
Wings (aircraft)
Multiobjective evolutionary algorithms
Surrogate models
Aeroelasticity
Aircraft wing
Estimation of distribution algorithm
ISSN:0177-0667, 1435-5663
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Shrnutí:This paper proposes a new self-adaptive meta-heuristic (MH) algorithm for multiobjective optimisation. The adaptation is accomplished by means of estimation of distribution. The differential evolution reproduction strategy is modified and used in this dominance-based multiobjective optimiser whereas population-based incremental learning is used to estimate the control parameters. The new method is employed to solve aeroelastic multiobjective optimisation of an aircraft wing which optimises structural weight and flutter speed. Design variables in the aeroelastic design problem include thicknesses of ribs, spars and composite layers. Also, the ply orientation of the upper and lower composite skins are assigned as the design variables. Additional benchmark test problems are also use to validate the search performance of the proposed algorithm. The performance validation reveals that the proposed optimiser is among the state-of-the-art multiobjective meta-heuristics. The concept of using estimation of distribution algorithm for tuning meta-heuristic control parameters is efficient and effective and becomes a new direction for improving MH performance.
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ISSN:0177-0667
1435-5663
DOI:10.1007/s00366-020-01077-w