Particle Swarm Approach in Finding Optimum Aircraft Configuration

Blasi, Luciano; Core, Giuseppe Del

doi:10.2514/1.24399

Cited by 15 publications

(15 citation statements)

References 12 publications

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“…where r 1 and r 2 are vectors of random numbers in the range [0,1] and w, c 1 , and c 2 are the inertial weight, the cognitive parameter and social parameter, respectively, Blasi and Del Core (2007). With the new velocity of a particle calculated, the position of a particle at the next iteration can be easily found,…”

Section: The Basic Particle Swarmmentioning

confidence: 99%

“…They have been applied in the conceptual design of aircraft, Blasi and Del Core (2007), and compared favorably to genetic algorithms and simulated annealing in the optimization of airfoils, Ray and Tsai (2004), and analytical functions, see for example, Angeline (1998), Brandstatter and Baumgartner (2002) and Venter and SobieszcaznskiSobieski (2003).…”

Section: The Basic Particle Swarmmentioning

confidence: 99%

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The development of a hybridized particle swarm for kriging hyperparameter tuning

Toal

Bressloff

Keane

et al. 2011

Engineering Optimization

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Optimizations involving high fidelity simulations can become prohibitively expensive when an exhaustive search is employed. To remove this expense a surrogate model is often constructed. One of the most popular techniques for the construction of such a surrogate model is that of kriging. However, the construction of a kriging model requires the optimization of a multi-model likelihood function, the cost of which, can approach that of the high fidelity simulations upon which the model is based. The following paper describes the development of a hybridized particle swarm algorithm which aims to reduce the cost of this likelihood optimization by drawing on an efficient adjoint of the likelihood. This hybridized tuning strategy is compared to a number of other strategies with respect to the inverse design of an airfoil as well as the optimization of an airfoil for minimum drag at a fixed lift.

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Section: The Basic Particle Swarmmentioning

confidence: 99%

Section: The Basic Particle Swarmmentioning

confidence: 99%

The development of a hybridized particle swarm for kriging hyperparameter tuning

Toal

Bressloff

Keane

et al. 2011

Engineering Optimization

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show abstract

“…The application of conventional nature-inspired methods to aerodynamic optimization has been significant (see, for example [4,[39][40][41][42][43][44] ), however the use of niching methods is much less despite the clear motivation to locate multiple optima in aerodynamic problems. The main application of such approaches to date is from Obayashi et al [45] who used niching techniques to locate the pareto front of a multi-objective wing design problem.…”

Section: Introductionmentioning

confidence: 99%

Identifying Multiple Optima in Aerodynamic Design Spaces

Poole

Allen

Rendall

2018

2018 Multidisciplinary Analysis and Optimization Conference

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Parallel niching optimization algorithms are developed and applied to a multimodal aerodynamic optimization case to identify multiple optima in the design space. Previous work by the authors has presented niching optimization algorithms that use differential evolution with feasible selection as a basis that can identify multiple optima in constrained search spaces, which is necessary for aerodynamic optimization. In this paper, these algorithms are further developed for application to aerodynamic optimization by reformulating each to provide a parallel decomposition of the objective function evaluation at each iteration of the optimization process. These algorithms are tested on an analytical optimization problem. The parallel, constrained form of both local nearest-neighbourhood and local crowding are shown to be the best performing algorithms with a 99% confidence level. A variation on the AIAA ADODG case 6 multimodal wing optimization case is also studied. A multi-start gradient-based approach is used to show multimodality of the design space. The local nearest-neighbourhood-based algorithm is then applied to the aerodynamic optimization case and is able to successfully identify two minima in the design space, with one being close to the global minimum and one in a different part of the design space, which is a local minimum. * Senior Teaching Associate.

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“…The local search would be applied to this intermediate solution in order to reach the global solution. The binary PSO method has been applied to define a preliminary short/medium range aircraft configuration, fully compliant with given requirements, that allows a minimum direct operating cost (Blasi & Del Core, 2007). In this work they tested two different boundary conditions viz.…”

Section: Recent Applications Of Binary Psomentioning

confidence: 99%