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

Ant Colony Optimisation – A Proposed Solution Framework for the Capacitated Facility Location Problem

This thesis is a critical investigation into the development, application and evaluation of ant colony optimisation metaheuristics, with a view to solving a class of capacitated facility location problems. The study is comprised of three phases. The first sets the scene and motivation for research, which includes; key concepts of ant colony optimisation, a review of published academic materials and a research philosophy which provides a justification for a deductive empirical mode of study. This phase reveals that published results for existing facility location metaheuristics are often ambiguous or incomplete and there is no clear evidence of a dominant method. This clearly represents a gap in the current knowledge base and provides a rationale for a study that will contribute to existing knowledge, by determining if ant colony optimisation is a suitable solution technique for solving capacitated facility location problems. The second phase is concerned with the research, development and application of a variety of ant colony optimisation algorithms. Solution methods presented include combinations of approximate and exact techniques. The study identifies a previously untried ant hybrid scheme, which incorporates an exact method within it, as the most promising of techniques that were tested. Also a novel local search initialisation which relies on memory is presented. These hybridisations successfully solve all of the capacitated facility location test problems available in the OR-Library. The third phase of this study conducts an extensive series of run-time analyses, to determine the prowess of the derived ant colony optimisation algorithms against a contemporary cross-entropy technique. This type of analysis for measuring metaheuristic performance for the capacitated facility location problem is not evident within published materials. Analyses of empirical run-time distributions reveal that ant colony optimisation is superior to its contemporary opponent. All three phases of this thesis provide their own individual contributions to existing knowledge bases: the production of a series of run-time distributions will be a valuable resource for future researchers; results demonstrate that hybridisation of metaheuristics with exact solution methods is an area not to be ignored; the hybrid methods employed in this study ten years ago would have been impractical or infeasible; ant colony optimisation is shown to be a very flexible metaheuristic that can easily be adapted to solving mixed integer problems using hybridisation techniques

Hybrid Genetic Bees Algorithm applied to Single Machine Scheduling with Earliness and Tardiness Penalties

This paper presents a hybrid Genetic-Bees Algorithm based optimised solution for the single machine scheduling problem. The enhancement of the Bees Algorithm (BA) is conducted using the Genetic Algorithm's (GA's) operators during the global search stage. The proposed enhancement aims to increase the global search capability of the BA gradually with new additions. Although the BA has very successful implementations on various type of optimisation problems, it has found that the algorithm suffers from weak global search ability which increases the computational complexities on NP-hard type optimisation problems e.g. combinatorial/permutational type optimisation problems. This weakness occurs due to using a simple global random search operation during the search process. To reinforce the global search process in the BA, the proposed enhancement is utilised to increase exploration capability by expanding the number of fittest solutions through the genetical variations of promising solutions. The hybridisation process is realised by including two strategies into the basic BA, named as â\u80\u9creinforced global searchâ\u80\u9d and â\u80\u9cjumping functionâ\u80\u9d strategies. The reinforced global search strategy is the first stage of the hybridisation process and contains the mutation operator of the GA. The second strategy, jumping function strategy, consists of four GA operators as single point crossover, multipoint crossover, mutation and randomisation. To demonstrate the strength of the proposed solution, several experiments were carried out on 280 well-known single machine benchmark instances, and the results are presented by comparing to other well-known heuristic algorithms. According to the experiments, the proposed enhancements provides better capability to basic BA to jump from local minima, and GBA performed better compared to BA in terms of convergence and the quality of results. The convergence time reduced about 60% with about 30% better results for highly constrained jobs

An Analysis of the Genetic Algorithm and Abstract Search Space Visualisation

The Genetic Algorithm (Holland, 1975) is a powerful search technique based upon the principles of Darwinian evolution. In its simplest form the GA consists of three main operators - crossover, mutation and selection. The principal theoretical treatment of the Genetic Algorithm (GA) is provided by the Schema Theorem and building block hypothesis (Holland, 1975). The building block hypothesis describes the GA search process as the combination, sampling and recombination of fragments of solutions known as building blocks. The crossover operator is responsible for the combination of building blocks, whilst the selection operator allocates increasing numbers of samples to good building blocks. Thus the GA constructs the optimal (or near-optimal) solution from those fragments of solutions which are, in some sense, optimal. The first part of this thesis documents the development of a technique for the isolation of building blocks from the populations of the GA. This technique is shown to extract exactly those building blocks of interest - those which are sampled most regularly by the GA. These building blocks are used to empirically investigate the validity of the building block hypothesis. It is shown that good building blocks do not combine to form significantly better solution fragments than those resulting from the addition of randomly generated building blocks to good building blocks. This results casts some doubt onto the value of the building block hypothesis as an account of the GA search process (at least for the functions used during these experiments). The second part of this thesis describes an alternative account of the action of crossover. This account is an approximation of the geometric effect of crossover upon the population of samples maintained by the GA. It is shown that, for a simple function, this description of the crossover operator is sufficiently accurate to warrant further investigation. A pair of performance models for the GA upon this function are derived and shown to be accurate for a wide range of crossover schemes. Finally, the GA search process is described in terms of this account of the crossover operator and parallels are drawn with the search process of the simulated annealing algorithm (Kirkpatrick et al, 1983). The third and final part of this thesis describes a technique for the visualisation of high dimensional surfaces, such as are defined by functions of many parameters. This technique is compared to the statistical technique of projection pursuit regression (Friedman & Tukey, 1974) and is shown to compare favourably both in terms of computational expense and quantitative accuracy upon a wide range of test functions. A fundamental flaw of this technique is that it may produce poor visualisations when applied to functions with a small high frequency (or order) components

Ant colony optimisation : a proposed solution framework for the capacitated facility location problem

This thesis is a critical investigation into the development, application and evaluation of ant colony optimisation metaheuristics, with a view to solving a class of capacitated facility location problems. The study is comprised of three phases. The first sets the scene and motivation for research, which includes; key concepts of ant colony optimisation, a review of published academic materials and a research philosophy which provides a justification for a deductive empirical mode of study. This phase reveals that published results for existing facility location metaheuristics are often ambiguous or incomplete and there is no clear evidence of a dominant method. This clearly represents a gap in the current knowledge base and provides a rationale for a study that will contribute to existing knowledge, by determining if ant colony optimisation is a suitable solution technique for solving capacitated facility location problems. The second phase is concerned with the research, development and application of a variety of ant colony optimisation algorithms. Solution methods presented include combinations of approximate and exact techniques. The study identifies a previously untried ant hybrid scheme, which incorporates an exact method within it, as the most promising of techniques that were tested. Also a novel local search initialisation which relies on memory is presented. These hybridisations successfully solve all of the capacitated facility location test problems available in the OR-Library. The third phase of this study conducts an extensive series of run-time analyses, to determine the prowess of the derived ant colony optimisation algorithms against a contemporary cross-entropy technique. This type of analysis for measuring metaheuristic performance for the capacitated facility location problem is not evident within published materials. Analyses of empirical run-time distributions reveal that ant colony optimisation is superior to its contemporary opponent. All three phases of this thesis provide their own individual contributions to existing knowledge bases: the production of a series of run-time distributions will be a valuable resource for future researchers; results demonstrate that hybridisation of metaheuristics with exact solution methods is an area not to be ignored; the hybrid methods employed in this study ten years ago would have been impractical or infeasible; ant colony optimisation is shown to be a very flexible metaheuristic that can easily be adapted to solving mixed integer problems using hybridisation techniques.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Ant colony optimisation : a proposed solution framework for the capacitated facility location problem

This thesis is a critical investigation into the development, application and evaluation of ant colony optimisation metaheuristics, with a view to solving a class of capacitated facility location problems. The study is comprised of three phases. The first sets the scene and motivation for research, which includes; key concepts of ant colony optimisation, a review of published academic materials and a research philosophy which provides a justification for a deductive empirical mode of study. This phase reveals that published results for existing facility location metaheuristics are often ambiguous or incomplete and there is no clear evidence of a dominant method. This clearly represents a gap in the current knowledge base and provides a rationale for a study that will contribute to existing knowledge, by determining if ant colony optimisation is a suitable solution technique for solving capacitated facility location problems. The second phase is concerned with the research, development and application of a variety of ant colony optimisation algorithms. Solution methods presented include combinations of approximate and exact techniques. The study identifies a previously untried ant hybrid scheme, which incorporates an exact method within it, as the most promising of techniques that were tested. Also a novel local search initialisation which relies on memory is presented. These hybridisations successfully solve all of the capacitated facility location test problems available in the OR-Library. The third phase of this study conducts an extensive series of run-time analyses, to determine the prowess of the derived ant colony optimisation algorithms against a contemporary cross-entropy technique. This type of analysis for measuring metaheuristic performance for the capacitated facility location problem is not evident within published materials. Analyses of empirical run-time distributions reveal that ant colony optimisation is superior to its contemporary opponent. All three phases of this thesis provide their own individual contributions to existing knowledge bases: the production of a series of run-time distributions will be a valuable resource for future researchers; results demonstrate that hybridisation of metaheuristics with exact solution methods is an area not to be ignored; the hybrid methods employed in this study ten years ago would have been impractical or infeasible; ant colony optimisation is shown to be a very flexible metaheuristic that can easily be adapted to solving mixed integer problems using hybridisation techniques.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Adaptive algorithms for history matching and uncertainty quantification

Numerical reservoir simulation models are the basis for many decisions in regard to predicting, optimising, and improving production performance of oil and gas reservoirs. History matching is required to calibrate models to the dynamic behaviour of the reservoir, due to the existence of uncertainty in model parameters. Finally a set of history matched models are used for reservoir performance prediction and economic and risk assessment of different development scenarios. Various algorithms are employed to search and sample parameter space in history matching and uncertainty quantification problems. The algorithm choice and implementation, as done through a number of control parameters, have a significant impact on effectiveness and efficiency of the algorithm and thus, the quality of results and the speed of the process. This thesis is concerned with investigation, development, and implementation of improved and adaptive algorithms for reservoir history matching and uncertainty quantification problems. A set of evolutionary algorithms are considered and applied to history matching. The shared characteristic of applied algorithms is adaptation by balancing exploration and exploitation of the search space, which can lead to improved convergence and diversity. This includes the use of estimation of distribution algorithms, which implicitly adapt their search mechanism to the characteristics of the problem. Hybridising them with genetic algorithms, multiobjective sorting algorithms, and real-coded, multi-model and multivariate Gaussian-based models can help these algorithms to adapt even more and improve their performance. Finally diversity measures are used to develop an explicit, adaptive algorithm and control the algorithm’s performance, based on the structure of the problem. Uncertainty quantification in a Bayesian framework can be carried out by resampling of the search space using Markov chain Monte-Carlo sampling algorithms. Common critiques of these are low efficiency and their need for control parameter tuning. A Metropolis-Hastings sampling algorithm with an adaptive multivariate Gaussian proposal distribution and a K-nearest neighbour approximation has been developed and applied

Hybridised ant colony optimisation for job shop problem

Master'sMASTER OF ENGINEERIN

Safety system design optimisation

This thesis investigates the efficiency of a design optimisation scheme that is appropriate for systems which require a high likelihood of functioning on demand. Traditional approaches to the design of safety critical systems follow the preliminary design, analysis, appraisal and redesign stages until what is regarded as an acceptable design is achieved. For safety systems whose failure could result in loss of life it is imperative that the best use of the available resources is made and a system which is optimal, not just adequate, is produced. The object of the design optimisation problem is to minimise system unavailability through manipulation of the design variables, such that limitations placed on them by constraints are not violated. Commonly, with mathematical optimisation problem; there will be an explicit objective function which defines how the characteristic to be minimised is related to the variables. As regards the safety system problem, an explicit objective function cannot be formulated, and as such, system performance is assessed using the fault tree method. By the use of house events a single fault tree is constructed to represent the failure causes of each potential design to overcome the time consuming task of constructing a fault tree for each design investigated during the optimisation procedure. Once the fault tree has been constructed for the design in question it is converted to a BDD for analysis. A genetic algorithm is first employed to perform the system optimisation, where the practicality of this approach is demonstrated initially through application to a High-Integrity Protection System (HIPS) and subsequently a more complex Firewater Deluge System (FDS). An alternative optimisation scheme achieves the final design specification by solving a sequence of optimisation problems. Each of these problems are defined by assuming some form of the objective function and specifying a sub-region of the design space over which this function will be representative of the system unavailability. The thesis concludes with attention to various optimisation techniques, which possess features able to address difficulties in the optimisation of safety critical systems. Specifically, consideration is given to the use of a statistically designed experiment and a logical search approach