A self-adaptive genetic algorithm applied to multi-objective optimization of an airfoil

Genetic algorithms (GAs) have been used to tackle non-linear multi-objective optimization (MOO) problems successfully, but their success is governed by key parameters which have been shown to be sensitive to the nature of the particular problem, incorporating concerns such as the numbers of objectives and variables, and the size and topology of the search space, making it hard to determine the best settings in advance. This work describes a real-encoded multi-objective optimizing GA (MOGA) that uses self-adaptive mutation and crossover, and which is applied to optimization of an airfoil, for minimization of drag and maximization of lift coefficients. The MOGA is integrated with a Free-Form Deformation tool to manage the section geometry, and XFoil which evaluates each airfoil in terms of its aerodynamic efficiency. The performance is compared with those of the heuristic MOO algorithms, the Multi-Objective Tabu Search (MOTS) and NSGA-II, showing that this GA achieves better convergence.

A preliminary study of a new multi-objective optimization algorithm

This paper presents a preliminary study which describes and evaluates a multi-objective (MO) version of a recently created single objective (SO) optimization algorithm called the "Alliance Algorithm" (AA). The algorithm is based on the metaphorical idea that several tribes, with certain skills and resource needs, try to conquer an environment for their survival and to ally together to improve the likelihood of conquest. The AA has given promising results in several fields to which has been applied, thus the development of a MO variant (MOAA) is a natural extension. Here the MOAA's performance is compared with two well-known MO algorithms: NSGA-II and SPEA-2. The performance measures chosen for this study are the convergence and diversity metrics. The benchmark functions chosen for the comparison are from the ZDT and OKA families and the main classical MO problems. The results show that the three algorithms have similar overall performance. Thus, it is not possible to identify a best algorithm for all the problems; the three algorithms show a certain complementarity because they offer superior performance for different classes of problems. © 2012 IEEE.

The design of multi-element airfoils through multi-objective optimization techniques

This paper presents the development and the application of a multi-objective optimization framework for the design of two-dimensional multi-element high-lift airfoils. An innovative and efficient optimization algorithm, namely Multi-Objective Tabu Search (MOTS), has been selected as core of the framework. The flow-field around the multi-element configuration is simulated using the commercial computational fluid dynamics (cfd) suite Ansys cfx. Elements shape and deployment settings have been considered as design variables in the optimization of the Garteur A310 airfoil, as presented here. A validation and verification process of the cfd simulation for the Garteur airfoil is performed using available wind tunnel data. Two design examples are presented in this study: a single-point optimization aiming at concurrently increasing the lift and drag performance of the test case at a fixed angle of attack and a multi-point optimization. The latter aims at introducing operational robustness and off-design performance into the design process. Finally, the performance of the MOTS algorithm is assessed by comparison with the leading NSGA-II (Non-dominated Sorting Genetic Algorithm) optimization strategy. An equivalent framework developed by the authors within the industrial sponsor environment is used for the comparison. To eliminate cfd solver dependencies three optimum solutions from the Pareto optimal set have been cross-validated. As a result of this study MOTS has been demonstrated to be an efficient and effective algorithm for aerodynamic optimizations. Copyright © 2012 Tech Science Press.

Multi-objective optimization for multiphase orbital rendezvous missions

A multi-objective optimization approach was proposed for multiphase orbital rendezvous missions and validated by application to a representative numerical problem. By comparing the Pareto fronts obtained using the proposed method, the relationships between the three objectives considered are revealed, and the influences of other mission parameters, such as the sensors' field of view, can also be analyzed effectively. For multiphase orbital rendezvous missions, the tradeoff relationships between the total velocity increment and the trajectory robustness index as well as between the total velocity increment and the time of flight are obvious and clear. However, the tradeoff relationship between the time of flight and the trajectory robustness index is weak, especially for the four- and five-phase missions examined. The proposed approach could be used to reorganize a stable rendezvous profile for an engineering rendezvous mission, when there is a failure that prevents the completion of the nominal mission.

Multi-objective optimization of a fluid structure interaction benchmarking

The integration and application of a new multi-objective tabu search optimization algorithm for Fluid Structure Interaction (FSI) problems are presented. The aim is to enhance the computational design process for real world applications and to achieve higher performance of the whole system for the four considered objectives. The described system combines the optimizer with a well established FSI solver which is based on the fully implicit, monolithic formuFlation of the problem in the Arbitrary Lagrangian-Eulerian FEM approach. The proposed solver resolves the proposed uid-structure interaction benchmark which describes the self-induced elastic deformation of a beam attached to a cylinder in laminar channel ow. The optimized ow characteristics of the aforementioned geometrical arrangement illustrate the performance of the system in two dimensions. Special emphasis is given to the analysis of the simulation package, which is of high accuracy and is the core of application. The design process identifies the best combination of ow features for optimal system behavior and the most important objectives. In addition, the presented methodology has the potential to run in parallel, which will significantly speed-up the elapsed time. Finite Element Method (FEM), Fluid-Structure Interaction (FSI), Multi-Ojective Tabu search (MOTS2). Copyright © 2013 Tech Science Press.

Multi-objective foundation optimization and its application to pile reuse

Pile reuse has become an increasingly popular option in foundation design, mainly due to its potential cost and environmental benefits and the problem of underground congestion in urban areas. However, key geotechnical concerns remain regarding the behavior of reused piles and the modeling of foundation systems involving old and new piles to support building loads of the new structure. In this paper, a design and analysis tool for pile reuse projects will be introduced. The tool allows coupling of superstructure stiffness with the foundation model, and includes an optimization algorithm to obtain the best configuration of new piles to work alongside reused piles. Under the concept of Pareto Optimality, multi-objective optimization analyses can also reveal the relationship between material usage and the corresponding foundation performance, providing a series of reuse options at various foundation costs. The components of this analysis tool will be discussed and illustrated through a case history in London, where 110 existing piles are reused at a site to support the proposed new development. The case history reveals the difficulties faced by foundation reuse in urban areas and demonstrates the application of the design tool to tackle these challenges. © ASCE 2011.