Evolutionary multi-path routing for network lifetime and robustness in wireless sensor networks

publisher: Elsevier articletitle: Evolutionary multi-path routing for network lifetime and robustness in wireless sensor networks journaltitle: Ad Hoc Networks articlelink: http://dx.doi.org/10.1016/j.adhoc.2016.08.005 content_type: article copyright: © 2016 Elsevier B.V. All rights reserved

Redesign of Industrial Apparatus using Multi-Objective Bayesian Optimisation

This is the author accepted manuscriptIntroduction. Design optimisation using Computational Fluid Dynamics (CFD) often requires extremising multiple (and often conflicting) objectives simultaneously. For instance, a heat exchanger design will require maximising the heat transfer across the media, while minimising the pressure drop across the apparatus. In such cases, usually there is no unique solution, but a range of solutions trading off between the objectives. The set of solutions optimally trading off the objectives are known as the Pareto set, and in practice only an approximation of the set may be achieved. Multi-Objective Evolutionary Algorithms (MOEAs) are known to perform well in estimating the optimal Pareto set. However, they require thousands of function evaluations, which is impractical with computationally expensive simulations. An alternative is to use Multi-Objective Bayesian Optimisation (MOBO) method that has been proved to be an effective approach with limited budget on function evaluations [1]. In this work, we illustrate a newly developed MOBO framework in [1] with OpenFOAM 2.3.1 to locate a good estimation of the optimal Pareto set for a range of industrial cases.This work was supported by the UK Engineering and Physical Sciences Research Council (EPSRC) grant (reference number: EP/M017915/1) for the CEMPS, University of Exeter, UK

Application of multi-objective Bayesian shape optimisation to a sharp-heeled Kaplan draft tube

The draft tube of a hydraulic turbine plays an important role for the efficiency and power characteristics of the overall system. The shape of the draft tube affects its performance, resulting in an increasing need for data-driven optimisation for its design. In this paper, shape optimisation of an elbow-type draft tube is undertaken, combining Computational Fluid Dynamics and a multi-objective Bayesian methodology. The chosen design objectives were to maximise pressure recovery, and minimise wall-frictional losses along the geometry. The design variables were chosen to explore potential new designs, using a series of subdivision-curves and splines on the inflow cone, outer-heel, and diffuser. The optimisation run was performed under part-load for the Kaplan turbine. The design with the lowest energy-loss identified on the Pareto-front was found to have a straight tapered diffuser, chamfered heel, and a convex inflow cone. Analysis of the performance quantities showed the typically used energy-loss factor and pressure recovery were highly correlated in cases of constant outflow cross-sections, and therefore unsuitable for use of multi-objective optimisation. Finally, a number of designs were tested over a range of discharges. From this it was found that reducing the heel size increased the efficiency over a wider operating range

Data-driven multi-objective optimisation of coal-fired boiler combustion systems

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record.Coal remains an important energy source. Nonetheless, pollutant emissions – in particular Oxides of Nitrogen (NOx) – as a result of the combustion process in a boiler, are subject to strict legislation due to their damaging effects on the environment. Optimising combustion parameters to achieve a lower NOx emission often results in combustion inefficiency measured with the proportion of unburned coal content (UBC). Consequently there is a range of solutions that trade-off efficiency for emissions. Generally, an analytical model for NOx emission or UBC is unavailable, and therefore data-driven models are used to optimise this multi-objective problem. We introduce the use of Gaussian process models to capture the uncertainties in NOx and UBC predictions arising from measurement error and data scarcity. A novel evolutionary multi-objective search algorithm is used to discover the probabilistic trade-off front between NOx and UBC, and we describe a new procedure for selecting parameters yielding the desired performance. We discuss the variation of operating parameters along the trade-off front. We give a novel algorithm for discovering the optimal trade-off for all load demands simultaneously. The methods are demonstrated on data collected from a boiler in Jianbi power plant, China, and we show that a wide range of solutions trading-off NOx and efficiency may be efficiently located.This work was supported by the Engineering and Physical Sciences Research Council, United Kingdom [Grant No.: EP/M017915/1], the National Natural Science Foundation of China [Grant Nos.: 61375078 and 61304211], and the China Scholarship Council

Greed is Good: Exploration and Exploitation Trade-offs in Bayesian Optimisation

This is the author accepted manuscript. The final version is available from ACM via the DOI in this recordThe performance of acquisition functions for Bayesian optimisation is investigated in terms of the Pareto front between exploration and exploitation. We show that Expected Improvement and the Upper Confidence Bound always select solutions to be expensively evaluated on the Pareto front, but Probability of Improvement is never guaranteed to do so and Weighted Expected Improvement does only for a restricted range of weights. We introduce two novel -greedy acquisition functions. Extensive empirical evaluation of these together with random search, purely exploratory and purely exploitative search on 10 benchmark problems in 1 to 10 dimensions shows that -greedy algorithms are generally at least as effective as conventional acquisition functions, particularly with a limited budget. In higher dimensions -greedy approaches are shown to have improved performance over conventional approaches. These results are borne out on a real world computational fluid dynamics optimisation problem and a robotics active learning problem.Innovate U

Applied Gaussian Process in Optimizing Unburned Carbon Content in Fly Ash for Boiler Combustion

This is the final version of the article. Available from Hindawi Publishing Corporation via the DOI in this record.Recently, Gaussian Process (GP) has attracted generous attention from industry. This article focuses on the application of coal fired boiler combustion and uses GP to design a strategy for reducing Unburned Carbon Content in Fly Ash (UCC-FA) which is the most important indicator of boiler combustion efficiency. With getting rid of the complicated physical mechanisms, building a data-driven model as GP is an effective way for the proposed issue. Firstly, GP is used to model the relationship between the UCC-FA and boiler combustion operation parameters. The hyperparameters of GP model are optimized via Genetic Algorithm (GA). Then, served as the objective of another GA framework, the predicted UCC-FA from GP model is utilized in searching the optimal operation plan for the boiler combustion. Based on 670 sets of real data from a high capacity tangentially fired boiler, two GP models with 21 and 13 inputs, respectively, are developed. In the experimental results, the model with 21 inputs provides better prediction performance than that of the other. Choosing the results from 21-input model, the UCC-FA decreases from 2.7% to 1.7% via optimizing some of the operational parameters, which is a reasonable achievement for the boiler combustion.This study was supported by the State Nature Science Foundation of China (no. 61375078; no. 61304211) and China Scholarship Council

MBORE: Multi-objective Bayesian Optimisation by Density-Ratio Estimation

This is the author accepted manuscript,The dataset associated with this article is available in ORE at: https://doi.org/10.24378/exe.3943Optimisation problems often have multiple conflicting objectives that can be computationally and/or financially expensive. Mono-surrogate Bayesian optimisation (BO) is a popular model-based approach for optimising such black-box functions. It combines objective values via scalarisation and builds a Gaussian process (GP) surrogate of the scalarised values. The location which maximises a cheap-to-query acquisition function is chosen as the next location to expensively evaluate. While BO is an effective strategy, the use of GPs is limiting. Their performance decreases as the problem input dimensionality increases, and their computational complexity scales cubically with the amount of data. To address these limitations, we extend previous work on BO by density-ratio estimation (BORE) to the multi-objective setting. BORE links the computation of the probability of improvement acquisition function to that of probabilistic classification. This enables the use of state-of-the-art classifiers in a BO-like framework. In this work we present MBORE: multi-objective Bayesian optimisation by density-ratio estimation, and compare it to BO across a range of synthetic and real-world benchmarks. We find that MBORE performs as well as or better than BO on a wide variety of problems, and that it outperforms BO on high-dimensional and real-world problems.Engineering and Physical Sciences Research Council (EPSRC

MBORE: Multi-objective Bayesian Optimisation by Density-Ratio Estimation (Optimisation Results)

Optimisation results for the paper “MBORE: Multi-objective Bayesian Optimisation by Density-Ratio Estimation”. The corresponding code to generate the results is available at: https://github.com/georgedeath/mbore/The article associated with this dataset is available in ORE at: http://hdl.handle.net/10871/129219This is the optimisation results for the De Ath et al. (2022) paper “MBORE: Multi-objective Bayesian Optimisation by Density-Ratio Estimation”.Engineering and Physical Sciences Research Council (EPSRC

In vitrotoxic effects of reduced graphene oxide nanosheets on lung cancer cells

journal_title: Nanotechnology article_type: paper article_title: toxic effects of reduced graphene oxide nanosheets on lung cancer cells copyright_information: © 2017 IOP Publishing Ltd date_received: 2017-08-30 date_accepted: 2017-10-24 date_epub: 2017-11-2