An asynchronous metamodel-assisted memetic algorithm for CFD-based shape optimization

Kontoleontos, E; Asouti, Varvara; Giannakoglou, Kyriakos C.

doi:10.1080/0305215x.2011.570758

Cited by 14 publications

(19 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With respect to the combination of global and local search methods within the design optimization process, the so-called hierarchical approach has been proposed in recent papers [18,19]. Metamodel-assisted memetic algorithms are hybrid schemes that combine, for example, stochastic methods for the exhaustive search of the design space along with gradient-based methods for the refinement of promising solutions.…”

Section: Literature Reviewmentioning

confidence: 99%

Aerodynamic Shape Design by Evolutionary Optimization and Support Vector Machines

Andrés-Pérez

Carro-Calvo

Salcedo‐Sanz

et al. 2016

Springer Tracts in Mechanical Engineering

View full text Add to dashboard Cite

This paper proposes a computational methodology for the aerodynamic shape design of aeronautical configurations, aiming a broad and efficient exploration of the design space. A novel adaptive sampling technique focused on the global optimization problem, the Intelligent Estimation Search with Sequential Learning (IES-SL), is presented. This approach is based on the use of Support Vector Machines (SVMs) as the surrogate model for estimating the objective function, in combination with an evolutionary algorithm (EA) to enable the discovery of global optima. The proposed methodology is applied to improve the aerodynamic performance of a two-dimensional airfoil and a three-dimensional wing and results on surrogate model validation and optimization-focused sampling criteria are discussed.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Aerodynamic Shape Design by Evolutionary Optimization and Support Vector Machines

Andrés-Pérez

Carro-Calvo

Salcedo‐Sanz

et al. 2016

Springer Tracts in Mechanical Engineering

View full text Add to dashboard Cite

show abstract

“…Apart from that, there exist optimisers with the use of grid-based computing integrating nongeneration evolutionary algorithms, surrogate models, local search, and Lamarckian learning process together. This approach is termed asynchronous metamodel-based evolutionary algorithms [36,37]. A technique used in the design of computer experiment phase is usually a Latin hypercube sampling technique.…”

Section: Introductionmentioning

confidence: 99%

Surrogate-Assisted Multiobjective Evolutionary Algorithms for Structural Shape and Sizing Optimisation

Kunakote

Bureerat

2013

Mathematical Problems in Engineering

View full text Add to dashboard Cite

The work in this paper proposes the hybridisation of the well-established strength Pareto evolutionary algorithm (SPEA2) and some commonly used surrogate models. The surrogate models are introduced to an evolutionary optimisation process to enhance the performance of the optimiser when solving design problems with expensive function evaluation. Several surrogate models including quadratic function, radial basis function, neural network, and Kriging models are employed in combination with SPEA2 using real codes. The various hybrid optimisation strategies are implemented on eight simultaneous shape and sizing design problems of structures taking into account of structural weight, lateral bucking, natural frequency, and stress. Structural analysis is carried out by using a finite element procedure. The optimum results obtained are compared and discussed. The performance assessment is based on the hypervolume indicator. The performance of the surrogate models for estimating design constraints is investigated. It has been found that, by using a quadratic function surrogate model, the optimiser searching performance is greatly improved.

show abstract

“…The use of a surrogate model [37,38] to better initialize the search or filter-out non-promising candidate solutions.…”

mentioning

confidence: 99%

Multi-Objective Climb Path Optimization for Aircraft/Engine Integration Using Particle Swarm Optimization

2017

View full text Add to dashboard Cite

Abstract:In this article, a new multi-objective approach to the aircraft climb path optimization problem, based on the Particle Swarm Optimization algorithm, is introduced to be used for aircraft-engine integration studies. This considers a combination of a simulation with a traditional Energy approach, which incorporates, among others, the use of a proposed path-tracking scheme for guidance in the Altitude-Mach plane. The adoption of population-based solver serves to simplify case setup, allowing for direct interfaces between the optimizer and aircraft/engine performance codes. A two-level optimization scheme is employed and is shown to improve search performance compared to the basic PSO algorithm. The effectiveness of the proposed methodology is demonstrated in a hypothetic engine upgrade scenario for the F-4 aircraft considering the replacement of the aircraft's J79 engine with the EJ200; a clear advantage of the EJ200-equipped configuration is unveiled, resulting, on average, in 15% faster climbs with 20% less fuel.

show abstract

An asynchronous metamodel-assisted memetic algorithm for CFD-based shape optimization

Cited by 14 publications

References 44 publications

Aerodynamic Shape Design by Evolutionary Optimization and Support Vector Machines

Aerodynamic Shape Design by Evolutionary Optimization and Support Vector Machines

Surrogate-Assisted Multiobjective Evolutionary Algorithms for Structural Shape and Sizing Optimisation

Multi-Objective Climb Path Optimization for Aircraft/Engine Integration Using Particle Swarm Optimization

Contact Info

Product

Resources

About