State-of-the-art in aerodynamic shape optimisation methods

Aerodynamic optimisation has become an indispensable component for any aerodynamic design over the past 60 years, with applications to aircraft, cars, trains, bridges, wind turbines, internal pipe flows, and cavities, among others, and is thus relevant in many facets of technology. With advancements in computational power, automated design optimisation procedures have become more competent, however, there is an ambiguity and bias throughout the literature with regards to relative performance of optimisation architectures and employed algorithms. This paper provides a well-balanced critical review of the dominant optimisation approaches that have been integrated with aerodynamic theory for the purpose of shape optimisation. A total of 229 papers, published in more than 120 journals and conference proceedings, have been classified into 6 different optimisation algorithm approaches. The material cited includes some of the most well-established authors and publications in the field of aerodynamic optimisation. This paper aims to eliminate bias toward certain algorithms by analysing the limitations, drawbacks, and the benefits of the most utilised optimisation approaches. This review provides comprehensive but straightforward insight for non-specialists and reference detailing the current state for specialist practitioners

Automated optimization of reconfigurable designs

Currently, the optimization of reconfigurable design parameters is typically done manually and often involves substantial amount effort. The main focus of this thesis is to reduce this effort. The designer can focus on the implementation and design correctness, leaving the tools to carry out optimization. To address this, this thesis makes three main contributions. First, we present initial investigation of reconfigurable design optimization with the Machine Learning Optimizer (MLO) algorithm. The algorithm is based on surrogate model technology and particle swarm optimization. By using surrogate models the long hardware generation time is mitigated and automatic optimization is possible. For the first time, to the best of our knowledge, we show how those models can both predict when hardware generation will fail and how well will the design perform. Second, we introduce a new algorithm called Automatic Reconfigurable Design Efficient Global Optimization (ARDEGO), which is based on the Efficient Global Optimization (EGO) algorithm. Compared to MLO, it supports parallelism and uses a simpler optimization loop. As the ARDEGO algorithm uses multiple optimization compute nodes, its optimization speed is greatly improved relative to MLO. Hardware generation time is random in nature, two similar configurations can take vastly different amount of time to generate making parallelization complicated. The novelty is efficient use of the optimization compute nodes achieved through extension of the asynchronous parallel EGO algorithm to constrained problems. Third, we show how results of design synthesis and benchmarking can be reused when a design is ported to a different platform or when its code is revised. This is achieved through the new Auto-Transfer algorithm. A methodology to make the best use of available synthesis and benchmarking results is a novel contribution to design automation of reconfigurable systems.Open Acces

A self-learning particle swarm optimizer for global optimization problems

Copyright @ 2011 IEEE. All Rights Reserved. This article was made available through the Brunel Open Access Publishing Fund.Particle swarm optimization (PSO) has been shown as an effective tool for solving global optimization problems. So far, most PSO algorithms use a single learning pattern for all particles, which means that all particles in a swarm use the same strategy. This monotonic learning pattern may cause the lack of intelligence for a particular particle, which makes it unable to deal with different complex situations. This paper presents a novel algorithm, called self-learning particle swarm optimizer (SLPSO), for global optimization problems. In SLPSO, each particle has a set of four strategies to cope with different situations in the search space. The cooperation of the four strategies is implemented by an adaptive learning framework at the individual level, which can enable a particle to choose the optimal strategy according to its own local fitness landscape. The experimental study on a set of 45 test functions and two real-world problems show that SLPSO has a superior performance in comparison with several other peer algorithms.This work was supported by the Engineering and Physical Sciences Research Council of U.K. under Grants EP/E060722/1 and EP/E060722/2