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.

Application of the multi-objective Alliance algorithm to a benchmark aerodynamic optimization problem

This paper introduces a new version of the multiobjective Alliance Algorithm (MOAA) applied to the optimization of the NACA 0012 airfoil section, for minimization of drag and maximization of lift coefficients, based on eight section shape parameters. Two software packages are used: XFoil which evaluates each new candidate airfoil section in terms of its aerodynamic efficiency, and a Free-Form Deformation tool to manage the section geometry modifications. Two versions of the problem are formulated with different design variable bounds. The performance of this approach is compared, using two indicators and a statistical test, with that obtained using NSGA-II and multi-objective Tabu Search (MOTS) to guide the optimization. The results show that the MOAA outperforms MOTS and obtains comparable results with NSGA-II on the first problem, while in the other case NSGA-II is not able to find feasible solutions and the MOAA is able to outperform MOTS. © 2013 IEEE.

Enhancing and developing the practical optimisation capabilities and intelligence of automatic design software

In the modern engineering design cycle the use of computational tools becomes a neces- sity. The complexity of the engineering systems under consideration for design increases dramatically as the demands for advanced and innovative design concepts and engineering products is expanding. At the same time the advancements in the available technology in terms of computational resources and power, as well as the intelligence of the design software, accommodate these demands and make them a viable approach towards the chal- lenge of real-world engineering problems. This class of design optimisation problems is by nature multi-disciplinary. In the present work we establish enhanced optimisation capabil- ities within the Nimrod/O tool for massively distributed execution of computational tasks through cluster and computational grid resources, and develop the potential to combine and benefit from all the possible available technological advancements, both software and hardware. We develop the interface between a Free Form Deformation geometry manage- ment in-house code with the 2D airfoil aerodynamic efficiency evaluation tool XFoil, and the well established multi-objective heuristic optimisation algorithm NSGA-II. A simple airfoil design problem has been defined to demonstrate the functionality of the design sys- tem, but also to accommodate a framework for future developments and testing with other state-of-the-art optimisation algorithms such as the Multi-Objective Genetic Algorithm (MOGA) and the Multi-Objective Tabu Search (MOTS) techniques. Ultimately, heav- ily computationally expensive industrial design cases can be realised within the presented framework that could not be investigated before. © 2012 by the authors. Published by the American Institute of Aeronautics and Astronautics, Inc.

Enhancing and developing the practical optimisation capabilities and intelligence of automatic design software

In the modern engineering design cycle the use of computational tools becomes a necessity. The complexity of the engineering systems under consideration for design increases dramatically as the demands for advanced and innovative design concepts and engineering products is expanding. At the same time the advancements in the available technology in terms of computational resources and power, as well as the intelligence of the design software, accommodate these demands and make them a viable approach towards the challenge of real-world engineering problems. This class of design optimisation problems is by nature multi-disciplinary. In the present work we establish enhanced optimisation capabilities within the Nimrod/O tool for massively distributed execution of computational tasks through cluster and computational grid resources, and develop the potential to combine and benefit from all the possible available technological advancements, both software and hardware. We develop the interface between a Free Form Deformation geometry management in-house code with the 2D airfoil aerodynamic efficiency evaluation tool XFoil, and the well established multi-objective heuristic optimisation algorithm NSGA-II. A simple airfoil design problem has been defined to demonstrate the functionality of the design system, but also to accommodate a framework for future developments and testing with other state-of-the-art optimisation algorithms such as the Multi-Objective Genetic Algorithm (MOGA) and the Multi-Objective Tabu Search (MOTS) techniques. Ultimately, heavily computationally expensive industrial design cases can be realised within the presented framework that could not be investigated before. ©2012 AIAA.

Testing of the multi-objective alliance algorithm on benchmark functions

A new version of the Multi-objective Alliance Algorithm (MOAA) is described. The MOAA's performance is compared with that of NSGA-II using the epsilon and hypervolume indicators to evaluate the results. The benchmark functions chosen for the comparison are from the ZDT and DTLZ families and the main classical multi-objective (MO) problems. The results show that the new MOAA version is able to outperform NSGA-II on almost all the problems.

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.

Vortex breaking and cutting in type II superconductors.

In technological superconductors, the Lorentz force on the flux vortices is opposed by inhomogeneous pinning and so the critical current may be controlled by a combination of vortex entanglement, cutting, and cross-joining. To understand the roles of these processes we report measurements of structures in which a weak pinning layer is sandwiched between two strongly pinning leads. Quantitative modeling of the results demonstrates that in such systems the critical current is limited by the deformation of individual vortices and not by subsequent cross-joining processes.