A Review of Surrogate Assisted Multiobjective Evolutionary Algorithms

Multiobjective evolutionary algorithms have incorporated surrogate models in order to reduce the number of required evaluations to approximate the Pareto front of computationally expensive multiobjective optimization problems. Currently, few works have reviewed the state of the art in this topic. However, the existing reviews have focused on classifying the evolutionary multiobjective optimization algorithms with respect to the type of underlying surrogate model. In this paper, we center our focus on classifying multiobjective evolutionary algorithms with respect to their integration with surrogate models. This interaction has led us to classify similar approaches and identify advantages and disadvantages of each class

A Review of Surrogate Assisted Multiobjective Evolutionary Algorithms

Multiobjective evolutionary algorithms have incorporated surrogate models in order to reduce the number of required evaluations to approximate the Pareto front of computationally expensive multiobjective optimization problems. Currently, few works have reviewed the state of the art in this topic. However, the existing reviews have focused on classifying the evolutionary multiobjective optimization algorithms with respect to the type of underlying surrogate model. In this paper, we center our focus on classifying multiobjective evolutionary algorithms with respect to their integration with surrogate models. This interaction has led us to classify similar approaches and identify advantages and disadvantages of each class

Beam selection for stereotactic ablative radiotherapy using Cyberknife with multileaf collimation.

The Cyberknife system (Accuray Inc., Sunnyvale, CA) enables radiotherapy using stereotactic ablative body radiotherapy (SABR) with a large number of non-coplanar beam orientations. Recently, a multileaf collimator has also been available to allow flexibility in field shaping. This work aims to evaluate the quality of treatment plans obtainable with the multileaf collimator. Specifically, the aim is to find a subset of beam orientations from a predetermined set of candidate directions, such that the treatment quality is maintained but the treatment time is reduced. An evolutionary algorithm is used to successively refine a randomly selected starting set of beam orientations. By using an efficient computational framework, clinically useful solutions can be found in several hours. It is found that 15 beam orientations are able to provide treatment quality which approaches that of the candidate beam set of 110 beam orientations, but with approximately half of the estimated treatment time. Choice of an efficient subset of beam orientations offers the possibility to improve the patient experience and maximise the number of patients treated

Surrogate Model Optimisation for PWR Fuel Management

Pressurised Water Reactor (PWR) fuel management is an operational problem for nuclear operators, requiring solutions on a regular basis throughout the life of the plant. A variety of conflicting factors and changing goals mean that fuel loading pattern design problems are multiobjective and, by design, have many input variables. This causes a combinatorial explosion, known as the ‘curse of dimensionality’, which makes these complex problems difficult to investigate. In this thesis, the method of surrogate model optimisation is adapted to PWR loading pattern generation. Surrogate models are developed based around three approaches: deep learning methods (convolutional neural networks and multi-layer perceptrons), the fission matrix and simulated quantum annealing. The models are used to predict core parameters of reactors in simplified optimisation scenarios for a microcore, a small modular reactor, and a ‘standard’ PWR. The experiments with deep learning models show that competitive results can be obtained for training sets using a much lower number of simulations than direct optimisation. Fission matrix experiments demonstrate the method to predict core parameters for the first time, with interesting preliminary results. Novel experiments using simulated quantum annealing demonstrate the technique is able to generate loading patterns by following heuristic rules and is suitable for application to custom optimisation hardware. The principal contribution of this work is to show that surrogate model optimisation can be used to augment fuel loading pattern optimisation, generating competitive results and providing enormous computational cost reduction and thus permitting more investigation within a given computational budget. These methods can also make use of new computational hardware such as neural chips and quantum annealers. The promising methods developed in this thesis thus provide candidate implementations that can bring the benefits of these innovations to the sphere of nuclear engineering

Particle Swarm Optimization

Particle swarm optimization (PSO) is a population based stochastic optimization technique influenced by the social behavior of bird flocking or fish schooling.PSO shares many similarities with evolutionary computation techniques such as Genetic Algorithms (GA). The system is initialized with a population of random solutions and searches for optima by updating generations. However, unlike GA, PSO has no evolution operators such as crossover and mutation. In PSO, the potential solutions, called particles, fly through the problem space by following the current optimum particles. This book represents the contributions of the top researchers in this field and will serve as a valuable tool for professionals in this interdisciplinary field

Metamodel-based design optimization in industrial turbomachinery

Fans and Blowers community is experiencing, during those years, an incredible push in rethinking design approaches and strategies. The change in regulations on minimum efficiency grades and market requirements on even more customized products demand a changing in the way design in fan technology is perceived. In this context, even if synthetic approaches for fan design and analysis are still valuable tools, they need to be flanked by metamodels in order to overcome the limitations and criticism introduced by empirical relationships developed in the past for specific applications. In addition, by replacing computation-intensive functions with approximate surrogate models, it is possible to adopt advanced and nested optimization methods, such as those based on Evolutionary Algorithms, drastically improving the overall optimization computational time. Surrogate-based Optimizations based on Evolutionary Algorithm should become common practice in design optimization because of their capability of find optima in the design space, thanks to their intrinsic balance between exploitation and exploration. This work proposes methods for interweave elements of metamodeling techniques and multi-objective optimization problems with the synthetic approaches classically developed by the turbomachinery community. The entire Thesis can be ideally divided into two parts; the first gives a brief survey on the classical fan design and analysis approaches and reports two synthetic in-house codes for axial fan performance prediction. The second part present the state-of-the-art in metamodeling and optimization techniques, underlining the role of metamodeling in supporting design optimization and focusing in the more reliable and accurate framework for multi-objective optimization in fans engineering design

Managing radiotherapy treatment trade-offs using multi-criteria optimisation and data envelopment analysis

Techniques for managing trade-offs between tumour control and normal tissue sparing in radiotherapy treatment planning are reviewed and developed. Firstly, a quality control method based on data envelopment analysis is proposed. The method measures the improvement potential of a plan by comparing the plan to other reference plans. The method considers multiple criteria, including one that represents anatomical variations between patients. An application to prostate cases demonstrates the capability of the method in identifying plans with further improvement potential. A multi-criteria based planning technique that considers treatment delivery is then proposed. The method integrates column generation in the revised normal boundary intersection method, which projects a set of equidistant reference points onto the non-dominated set to form a representative set of non-dominated points. The delivery constraints are considered in the column generation process. Essentially, the method generates a set of deliverable plans featuring a range of treatment trade-offs. Demonstrated by a prostate case, the method generates near-optimal plans that can be delivered with dramatically lower total fluence than the optimal ones post-processed for treatment delivery constraints. Finally, a navigation method based on solving interactive multi-objective optimisation for a discrete set of plans is developed. The method sets the aspiration values for criteria as soft constraints, thus allowing the planner to freely express his/her preferences without causing infeasibility. Navigation is conducted on planner-defined clinical criteria, including the non-convex dose-volume criteria and treatment delivery time. Navigation steps on a prostate case are demonstrated with a prototype implementation. The prostate case shows that optimisation criteria may not correctly reflect plan quality and can mislead a planner to select a “sub-optimal” plan. Instead, using clinical criteria provides the most relevant measure of plan quality, hence allowing the planner to quickly identify the most preferable plan from a representative set

Evolutionary Algorithms and Computational Methods for Derivatives Pricing

This work aims to provide novel computational solutions to the problem of derivative pricing. To achieve this, a novel hybrid evolutionary algorithm (EA) based on particle swarm optimisation (PSO) and differential evolution (DE) is introduced and applied, along with various other state-of-the-art variants of PSO and DE, to the problem of calibrating the Heston stochastic volatility model. It is found that state-of-the-art DEs provide excellent calibration performance, and that previous use of rudimentary DEs in the literature undervalued the use of these methods. The use of neural networks with EAs for approximating the solution to derivatives pricing models is next investigated. A set of neural networks are trained from Monte Carlo (MC) simulation data to approximate the closed form solution for European, Asian and American style options. The results are comparable to MC pricing, but with offline evaluation of the price using the neural networks being orders of magnitudes faster and computationally more efficient. Finally, the use of custom hardware for numerical pricing of derivatives is introduced. The solver presented here provides an energy efficient data-flow implementation for pricing derivatives, which has the potential to be incorporated into larger high-speed/low energy trading systems

Towards a novel biologically-inspired cloud elasticity framework

With the widespread use of the Internet, the popularity of web applications has significantly increased. Such applications are subject to unpredictable workload conditions that vary from time to time. For example, an e-commerce website may face higher workloads than normal during festivals or promotional schemes. Such applications are critical and performance related issues, or service disruption can result in financial losses. Cloud computing with its attractive feature of dynamic resource provisioning (elasticity) is a perfect match to host such applications. The rapid growth in the usage of cloud computing model, as well as the rise in complexity of the web applications poses new challenges regarding the effective monitoring and management of the underlying cloud computational resources. This thesis investigates the state-of-the-art elastic methods including the models and techniques for the dynamic management and provisioning of cloud resources from a service provider perspective. An elastic controller is responsible to determine the optimal number of cloud resources, required at a particular time to achieve the desired performance demands. Researchers and practitioners have proposed many elastic controllers using versatile techniques ranging from simple if-then-else based rules to sophisticated optimisation, control theory and machine learning based methods. However, despite an extensive range of existing elasticity research, the aim of implementing an efficient scaling technique that satisfies the actual demands is still a challenge to achieve. There exist many issues that have not received much attention from a holistic point of view. Some of these issues include: 1) the lack of adaptability and static scaling behaviour whilst considering completely fixed approaches; 2) the burden of additional computational overhead, the inability to cope with the sudden changes in the workload behaviour and the preference of adaptability over reliability at runtime whilst considering the fully dynamic approaches; and 3) the lack of considering uncertainty aspects while designing auto-scaling solutions. This thesis seeks solutions to address these issues altogether using an integrated approach. Moreover, this thesis aims at the provision of qualitative elasticity rules. This thesis proposes a novel biologically-inspired switched feedback control methodology to address the horizontal elasticity problem. The switched methodology utilises multiple controllers simultaneously, whereas the selection of a suitable controller is realised using an intelligent switching mechanism. Each controller itself depicts a different elasticity policy that can be designed using the principles of fixed gain feedback controller approach. The switching mechanism is implemented using a fuzzy system that determines a suitable controller/- policy at runtime based on the current behaviour of the system. Furthermore, to improve the possibility of bumpless transitions and to avoid the oscillatory behaviour, which is a problem commonly associated with switching based control methodologies, this thesis proposes an alternative soft switching approach. This soft switching approach incorporates a biologically-inspired Basal Ganglia based computational model of action selection. In addition, this thesis formulates the problem of designing the membership functions of the switching mechanism as a multi-objective optimisation problem. The key purpose behind this formulation is to obtain the near optimal (or to fine tune) parameter settings for the membership functions of the fuzzy control system in the absence of domain experts’ knowledge. This problem is addressed by using two different techniques including the commonly used Genetic Algorithm and an alternative less known economic approach called the Taguchi method. Lastly, we identify seven different kinds of real workload patterns, each of which reflects a different set of applications. Six real and one synthetic HTTP traces, one for each pattern, are further identified and utilised to evaluate the performance of the proposed methods against the state-of-the-art approaches

Benchmarking renewable energy sources carbon savings and economic effectiveness

Over the last decade, the levelised cost of energy (LCOE) of many renewable technologies has sharply declined. As a result, direct cost comparisons of LCOE figures have made renewables to be perceived as economically very competitive options to decarbonise energy systems when compared to other low-carbon technologies such as Nuclear and Carbon Capture and Storage. We identify several theoretical shortcomings in relation to using LCOE or similar life-cycle economic metrics to make inferences about the relative economic effectiveness of using renewable technologies to decarbonise energy systems. We outline several circumstances in which the sole reliance on these metrics can lead to suboptimal or misguided investment and policymaking decisions. The thesis proposes a new theoretical framework to measure and benchmark the cost- effectiveness of decarbonising electric systems using renewables. The new framework is generic, technology-neutral, and enables consolidation of the results of decarbonisation studies that consider various renewable technologies and low carbon technologies. It also enables measuring and tracking the cost-effectiveness of the renewable decarbonisation process at a country or a system level. As a result, it also allows the direct comparison of the economic implications of different decarbonisation scenarios and various policy proposals in a very intuitive graphical way. In addition, the thesis proposes a new, unit-free metric, tentatively called Carbon Economic Effectiveness Credit (CEEC), to benchmark the relative cost-effectiveness of using different renewable technologies to achieve long-term carbon emission savings. Theoretically, CEEC represents the elasticity of the system total cost with respect to the carbon reduction savings attributable to renewables. In contrast to stand-alone, life-cycle metrics such as the LCOE, the proposed metric considers the economic and technical parameters of the renewable technologies and characteristic of the system under study. It also allows expressing the cost- effectiveness of the renewable decarbonisation process as a function of the system-wide decarbonisation level. Using historical load profiles, high-resolution solar radiation data and long-term meteorological data for a relatively small Gulf country, we investigate the deep decarbonisation of the electric system through the large-scale deployment of different renewables technologies. In particular, we use two well-established optimisation methodologies that have been used extensively in the literature to study the decarbonisation of power systems, namely: the screening curve (SC) method and the unit commitment (UC) method. In analysing the results of the two methodologies, we find that the choice of the modelling methodology, in some cases, can greatly influence the perceived carbon cost- effectiveness of renewables and subsequently their carbon abatement cost estimates. In particular, our results suggest that under deep decarbonisation scenarios, the estimate of the long-term carbon savings of renewables is strongly influenced by (1) the choice of the modelling method and (2) the technical specifications of the simulation models. Our results suggest that under deep decarbonisation scenarios, using simpler optimisation models may change the perceived economic effectiveness of renewables to decarbonise some electric systems. More importantly, our research sheds light on potential shortcomings in the current modelling practices and help identify patterns of possible inaccuracies or biases in renewable decarbonisation results. Moreover, our research suggests that the variations in the technical characteristics of renewable technologies can have a large influence on the economics of the decarbonisation process. We show that not all renewable technology types can have a suppressing effect on the variable costs of the systems due to their “zero marginal costs.” In particular, we identify certain technologies and circumstances in which an increase in renewable penetration can significantly inflate the variable energy costs of the system. More specifically, we find that under deep decarbonisation scenarios, renewable technologies with a highly volatile production profiles can act as an amplifier for the variable cost of the systems through (1) reducing the effectiveness of thermal generation units due the increased start-up and shutting downing activities, and (2) increasing the energy output levels from more flexible and yet more expensive thermal technologies. In addition, we identify circumstances in which an increased renewable penetration can materially affect the capacity adequacy of electric systems, leading to an increase in capacity investment in thermal flexibility assets. Perhaps more importantly, we find that these additional flexibility assets will not be commercially viable on an energy-output basis. We believe that this might have specific implications for the energy-only markets. Finally, we discuss the policy implications of our findings and propose several important recommendations. Altogether, we hope that our work will advance the understanding of the economics of climate change and integrating renewables into energy systems.Open Acces