An analysis of the interface between evolutionary algorithm operators and problem features for water resources problems. A case study in water distribution network design

Open Access articleEvolutionary Algorithms (EAs) have been widely employed to solve water resources problems for nearly two decades with much success. However, recent research in hyperheuristics has raised the possibility of developing optimisers that adapt to the characteristics of the problem being solved. In order to select appropriate operators for such optimisers it is necessary to first understand the interaction between operator and problem. This paper explores the concept of EA operator behaviour in real world applications through the empirical study of performance using water distribution networks (WDN) as a case study. Artificial networks are created to embody specific WDN features which are then used to evaluate the impact of network features on operator performance. The method extracts key attributes of the problem which are encapsulated in the natural features of a WDN, such as topologies and assets, on which different EA operators can be tested. The method is demonstrated using small exemplar networks designed specifically so that they isolate individual features. A set of operators are tested on these artificial networks and their behaviour characterised. This process provides a systematic and quantitative approach to establishing detailed information about an algorithm's suitability to optimise certain types of problem. The experiment is then repeated on real-world inspired networks and the results are shown to fit with the expected results.Engineering and Physical Sciences Research Council (EPSRC

Automated construction of evolutionary algorithm operators for the bi-objective water distribution network design problem using a genetic programming based hyper-heuristic approach

The water distribution network (WDN) design problem is primarily concerned with finding the optimal pipe sizes that provide the best service for minimal cost; a problem of continuing importance both in the UK and internationally. Consequently, many methods for solving this problem have been proposed in the literature, often using tailored, hand-crafted approaches to more effectively optimise this difficult problem. In this paper we investigate a novel hyper-heuristic approach that uses genetic programming (GP) to evolve mutation operators for evolutionary algorithms (EAs) which are specialised for a bi-objective formulation of the WDN design problem (minimising WDN cost and head deficit). Once generated, the evolved operators can then be used ad infinitum in any EA on any WDN to improve performance. A novel multi-objective method is demonstrated that evolves a set of mutation operators for one training WDN. The best operators are evaluated in detail by applying them to three test networks of varying complexity. An experiment is conducted in which 83 operators are evolved. The best 10 are examined in detail. One operator, GP1, is shown to be especially effective and incorporates interesting domain-specific learning (pipe smoothing) while GP5 demonstrates the ability of the method to find known, well-used operators like a Gaussian. © IWA Publishing 2014J.Engineering and Physical Sciences Research Council (EPSRC)Mouchel Ltd

Novel Methods for Ranking District Metered Areas for Water Distribution Network Maintenance Scheduling

Computing and Control for the Water Industry conference 2011 (CCWI 2011), University of Exeter, Exeter, UK, 5 - 7 September 2011To prevent the accumulation of material in pipes which leads to the potential for discolouration events to occur, UK water companies often operate five year cleaning schedules. To organise the schedule District Metered Areas (DMAs), the case study water company assigns a score based on several key performance indicators and water quality levels, which are used to place each DMA into one of three categories: good, poor and urgent. This paper investigates alternative methods of ranking DMAs in order to generate better maintenance schedules. We demonstrate how DMAs can be both partially and totally ordered with methods from multi-objective optimisation, and show how it is possible to prioritise and progressively apply Discolouration Propensity Modelling (DPM) to help guide interventions in the most effective and efficient way. Results obtained from sample DMAs show a good correlation between the DPM scores and the rankings produced by the multiobjective methods. We apply both methods to water networks from a UK water company and demonstrate that used in combination the power index and DPM, have advantages over the current ranking method

Tracking digital impact (TDI) tool.

The Tracking Digital Impact (TDI) tool is designed to help researchers, research groups, projects and institutions assess their current and future digital engagement strategies in an objective and informed way to support the development of new and improved strategies that more effectively enable good engagement with businesses, communities, the public, governing bodies and other researchers to facilitate better engagement and greater impact. The TDI tool was developed as part of a JISC funded project which focused on identifying, synthesising and embedding business, community and public (BCE) engagement best practices. The TDI tool examined the best practices at the dot.rural Digital Economies hub at the University of Aberdeen and translated those (accompanied by new guidance) into the TDI tool. Parts of this document were sourced from 'Brief Notes on Social Media for Research' by Jennifer Holden (University of Aberdeen, October, 2012). This document describes the TDI tool and its use

Tracking digital impact (TDI) tool: key questions reference.

This is a quick reference summary of the 'Key Questions' developed as part of the large Tracking Digital Impact (TDI) Tool. Users with experience of digital technologies or have previously completed the TDI tool may find this a useful reference when re-assessing or completing new assessments

Variable crustal structure along the Juan de Fuca Ridge : influence of on-axis hot spots and absolute plate motions

Author Posting. © American Geophysical Union, 2008. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Geochemistry Geophysics Geosystems 9 (2008): Q08001, doi:10.1029/2007GC001922.Multichannel seismic and bathymetric data from the Juan de Fuca Ridge (JDFR) provide constraints on axial and ridge flank structure for the past 4–8 Ma within three spreading corridors crossing Cleft, Northern Symmetric, and Endeavour segments. Along-axis data reveal south-to-north gradients in seafloor relief and presence and depth of the crustal magma lens, which indicate a warmer axial regime to the south, both on a regional scale and within individual segments. For young crust, cross-axis lines reveal differences between segments in Moho two-way traveltimes of 200–300 ms which indicate 0.5–1 km thicker crust at Endeavour and Cleft compared to Northern Symmetric. Moho traveltime anomalies extend beyond the 5–15 km wide axial high and coincide with distinct plateaus, 32 and 40 km wide and 200–400 m high, found at both segments. On older crust, Moho traveltimes are similar for all three segments (∼2100 ± 100 ms), indicating little difference in average crustal production prior to ∼0.6 and 0.7 Ma. The presence of broad axis-centered bathymetric plateau with thickened crust at Cleft and Endeavour segments is attributed to recent initiation of ridge axis-centered melt anomalies associated with the Cobb hot spot and the Heckle melt anomaly. Increased melt supply at Cleft segment upon initiation of Axial Volcano and southward propagation of Endeavour segment during the Brunhes point to rapid southward directed along-axis channeling of melt anomalies linked to these hot spots. Preferential southward flow of the Cobb and Heckle melt anomalies and the regional-scale south-to-north gradients in ridge structure along the JDFR may reflect influence of the northwesterly absolute motion of the ridge axis on subaxial melt distribution.This work was supported by U.S. National Science Foundation grants OCE00-02488 to S.M.C., OCE06-48303 to S.M.C. and M.R.N., OCE-0648923 to J.P.C., and OCE00-02600 to G.M.K. and A.J.H

Upper crustal evolution across the Juan de Fuca ridge flanks

Author Posting. © American Geophysical Union, 2008. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Geochemistry Geophysics Geosystems 9 (2008): Q09006, doi:10.1029/2008GC002085.Recent P wave velocity compilations of the oceanic crust indicate that the velocity of the uppermost layer 2A doubles or reaches ∼4.3 km/s found in mature crust in <10 Ma after crustal formation. This velocity change is commonly attributed to precipitation of low-temperature alteration minerals within the extrusive rocks associated with ridge-flank hydrothermal circulation. Sediment blanketing, acting as a thermal insulator, has been proposed to further accelerate layer 2A evolution by enhancing mineral precipitation. We carried out 1-D traveltime modeling on common midpoint supergathers from our 2002 Juan de Fuca ridge multichannel seismic data to determine upper crustal structure at ∼3 km intervals along 300 km long transects crossing the Endeavor, Northern Symmetric, and Cleft ridge segments. Our results show a regional correlation between upper crustal velocity and crustal age. The measured velocity increase with crustal age is not uniform across the investigated ridge flanks. For the ridge flanks blanketed with a sealing sedimentary cover, the velocity increase is double that observed on the sparsely and discontinuously sedimented flanks (∼60% increase versus ∼28%) over the same crustal age range of 5–9 Ma. Extrapolation of velocity-age gradients indicates that layer 2A velocity reaches 4.3 km/s by ∼8 Ma on the sediment blanketed flanks compared to ∼16 Ma on the flanks with thin and discontinuous sediment cover. The computed thickness gradients show that layer 2A does not thin and disappear in the Juan de Fuca region with increasing crustal age or sediment blanketing but persists as a relatively low seismic velocity layer capping the deeper oceanic crust. However, layer 2A on the fully sedimented ridge-flank sections is on average thinner than on the sparsely and discontinuously sedimented flanks (330 ± 80 versus 430 ± 80 m). The change in thickness occurs over a 10–20 km distance coincident with the onset of sediment burial. Our results also suggest that propagator wakes can have atypical layer 2A thickness and velocity. Impact of propagator wakes is evident in the chemical signature of the fluids sampled by ODP drill holes along the east Endeavor transect, providing further indication that these crustal discontinuities may be sites of localized fluid flow and alteration.This research was supported by National Science Foundation grants OCE-00-02488, OCE-00-02551, and OCE-00- 02600

Tracking Digital Impact. A Quick Introduction

Kent McClymont presented this paper as part of the University of Exeter's Open Access Week events.Kent McClymont, Associate Research Fellow in Computer Science, on the tracking digital impact project and data collection for publishing on Open Access

Multi-Objective Hyper-heuristics and their Application to Water Distribution Network Design

A thesis on the topic of multi-objective hyper-heuristics (selective and generative) applied to problems from hydro-informatics.Hyper-heuristics is a new field of optimisation which has recently emerged and is receiving growing exposure in the research community and literature. Hyper-heuristics are optimisation methods which are designed with a high level of abstraction from any one specific problem or class of problems and therefore are more generally applicable than specialised meta-heuristic and heuristic methods. Instead of being designed to solve a specific real-world problem, hyper-heuristics are designed to solve the problem of heuristic generation and selection. As such, hyper-heuristics can be thought of as methods for optimising the operations of an optimisation process which finds good solutions to a problem as a by-product. This approach has been shown to be very effective and in some cases provides improvement in search performance as well as reducing the burden associated with tailoring meta-heuristics which is often required when solving new problems. In this thesis, the hypothesis that hyper-heuristics can be competitively applied to real-world multi-objective optimisation problems such as the water distribution design problem is tested. Although many single-objective hyper-heuristics have been proposed in the literature, only a few multi-objective methods have been proposed. This thesis explores two different novel multi-objective hyper-heuristics: one designed for generating new specialised heuristics; and one designed for solving the online selection of heuristics. Firstly, the behaviour of a set of heuristics is explored to create a base understanding of different heuristic behavioural traits in order to better understand the hyper-heuristic behaviours and dynamics later in the study. Both approaches are tested on a range of benchmark optimisation problems and finally applied to real-world instances of the water distribution network design problem where the selective hyper-heuristics is demonstrated as being very effective at solving this difficult problem. Furthermore, the thesis demonstrates how heuristic selection can be improved by incorporating a greater level of information about heuristic performance, namely the historical joint performance of different heuristics, and shows that exploiting this sequencing information in heuristic selection can produce highly competitive results.EPSR

IRIS Case Study. Embedding Research Impact sub-programme, round 1.

In considering the interfacing research and impact systems (IRIS) problem, the IRIS team developed a new toolset (Figure 2), which empowers both researchers and research users. Built from an evidence base comprising an extensive literature review, a survey and two workshops, the IRIS toolset is composed of the five ‘MIMEE’ processes: 1. Modelling - What’s going on and who’s affected? 2. Identification – Where is it going on? 3. Monitoring - How do you know it’s going on? 4. Evidencing - Where and what should you exchange with people about what’s going on? 5. Exchanging - How do I exchange with people about what’s going on? The IRIS toolset was applied to two case study research projects located in education and engineering departments with diverse methodological and BCE research contexts. Deconstruction of both projects using the IRIS toolset was possible for both projects, demonstrating its value in a range of disciplinary and institutional contexts. Key points of learning are identified throughout the following report, but the principle capabilities developed and points of embedding are summarised below