Kent McClymont

Battle of the Water Networks II

The Battle of the Water Networks II (BWN-II) is the latest of a series of competitions related to the design and operation of water distribution systems (WDSs) undertaken within the Water Distribution Systems Analysis (WDSA) Symposium series. The BWN-II problem specification involved a broadly defined design and operation problem for an existing network that has to be upgraded for increased future demands, and the addition of a new development area. The design decisions involved addition of new and parallel pipes, storage, operational controls for pumps and valves, and sizing of backup power supply. Design criteria involved hydraulic, water quality, reliability, and environmental performance measures. Fourteen teams participated in the Battle and presented their results at the 14th Water Distribution Systems Analysis conference in Adelaide, Australia, September 2012. This paper summarizes the approaches used by the participants and the results they obtained. Given the complexity of the BWN-II problem and the innovative methods required to deal with the multiobjective, high dimensional and computationally demanding nature of the problem, this paper represents a snap-shot of state of the art methods for the design and operation of water distribution systems. A general finding of this paper is that there is benefit in using a combination of heuristic engineering experience and sophisticated optimization algorithms when tackling complex real-world water distribution system design problems

Deductive sort and climbing sort: New methods for non-dominated sorting

In recent years an increasing number of real-world many-dimensional optimisation problems have been identified across the spectrum of research fields. Many popular evolutionary algorithms use non-dominance as a measure for selecting solutions for future generations. The process of sorting populations into non-dominated fronts is usually the controlling order of computational complexity and can be expensive for large populations or for a high number of objectives. This paper presents two novel methods for non-dominated sorting: deductive sort and climbing sort. The two new methods are compared to the fast non-dominated sort of NSGA-II and the non-dominated rank sort of the omni-optimizer. The results demonstrate the improved efficiencies of the deductive sort and the reductions in comparisons that can be made when applying inferred dominance relationships defined in this paper.

Metabolic tinker: an online tool for guiding the design of synthetic metabolic pathways

One of the primary aims of synthetic biology is to (re)design metabolic pathways towards the production of desired chemicals. The fast pace of developments in molecular biology increasingly makes it possible to experimentally redesign existing pathways and implement de novo ones in microbes or using in vitro platforms. For such experimental studies, the bottleneck is shifting from implementation of pathways towards their initial design. Here, we present an online tool called ‘Metabolic Tinker’, which aims to guide the design of synthetic metabolic pathways between any two desired compounds. Given two user-defined ‘target’ and ‘source’ compounds, Metabolic Tinker searches for thermodynamically feasible paths in the entire known metabolic universe using a tailored heuristic search strategy. Compared with similar graph-based search tools, Metabolic Tinker returns a larger number of possible paths owing to its broad search base and fast heuristic, and provides for the first time thermodynamic feasibility information for the discovered paths. Metabolic Tinker is available as a web service at http://osslab.ex.ac.uk/tinker.aspx. The same website also provides the source code for Metabolic Tinker, allowing it to be developed further or run on personal machines for specific applications.

Markov chain hyper-heuristic (MCHH): an online selective hyper-heuristic for multi-objective continuous problems

In this paper we present the Markov chain Hyper-heuristic (MCHH), a novel online selective hyper-heuristic which employs reinforcement learning and Markov chains to provide an adaptive heuristic selection method. Experiments are conducted to demonstrate the efficacy of the method and comparisons are made with standard heuristics, a random hyper-heuristic and a multi-objective hyper-heuristic from the literature. The approaches are compared on a small number of evaluations of the multi-objective DTLZ test problems to reflect the computational limitations of expensive optimisation problems. The results demonstrate the MCHH robust and reliable performance on these problems.

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

Evolutionary 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 algorithms 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. Explores the relationship between search operators and problem spaces in water resources problems.Standard mutation robust to most changes within a water distribution network.Problem specific operators less robust to looping in networks than mutation.Combinations of operators with problem specific operators improve performance markedly.

Recent advances in problem understanding: changes in the landscape a year on

This paper provides an updated survey of new literature in, and related to, the field of problem understanding which has been published or made available since January 2012. The bibliographic information from the survey is available online at http://bit.ly/ZWoY3X. The survey covers work on the topics of: Benchmark Problems; Problem Decomposition & Multiobjectivisation; Landscape Analysis; Problem Difficulty; and Algorithm Selection & Performance Prediction. In addition, special attention is drawn to three recently published and excellent topic specific surveys. A side note is also made regarding the parallels between problem understanding, and specifically landscape analysis and the work of fitness landscape analysis in theoretical, conventional and evolutionary biology.

Benchmark multi-objective optimisation test problems with mixed encodings

The fields of multi-objective combinatorial and continuous optimisation have both experienced a significant and rapid growth in publications and research since the turn of the century. Despite this it is more often than not that methods in both fields are applied only to problems of one type of encoding. However, many real-world problems require parameters vectors that use multiple encodings. This paper presents a suite of novel multi-objective optimisation test problems (Exeter1 to 6) with mixed encodings (real and binary) and offer variable correlation between the objectives at different stages of the search. The problems are demonstrated using NSGA-II, SPEA2 and a (μ+λ) Evolution Strategy which were modified to operate on both encodings simultaneously.

The lay of the land: a brief survey of problem understanding

Optimisation research often concentrates on developing heuristic methods for solving a given optimisation problem, however a growing body of work surrounds the understanding and analysis of the problem itself to facilitate heuristic development and selection. We outline the broad themes that this field encompasses, presenting some of the techniques and approaches that have been taken, and present a sociometric view of selected publications in the field. We conclude by proposing a collaborative system to allow researchers working on problem understanding to more easily share results and work together.

Optimising multi-modal polynomial mutation operators for multi-objective problem classes

This paper presents a novel method of generating new probability distributions tailored to specific problem classes for use in optimisation mutation operators. A range of tailored operators with varying behaviours are created using the proposed technique and the evolved multi-modal polynomial distributions are found to match the performance of a tuned Gaussian distribution when applied to a mutation operator incorporated in a simple (1+1) Evolution Strategy. The generated heuristics are shown to display a range of desirable characteristics for the DTLZ test problems 1, 2 and 7; such as speed of convergence.

Biaxial box plots and ordered trial ranks for visualizing large sets of experimental results

This paper presents a novel method for visualizing large experimental datasets called a Biaxial Box Plot which provides both an easily read general impression of the results that highlights performance trends whilst also allowing for careful comparison of individual results. The Biaxial Box Plot is compared against heatmaps and traditional box plots where it is argued that the new method provides a suitable combination of the two existing methods. In addition, a novel ranking method is presented called the Ordered Trial Rank (OTR) that is designed for use with results that contain a large number of related sets of samples - e.g. a group of algorithm performance results on the same problem. The OTR is compared against simple median and standard deviation scores and shown to provide a better statistical distinction between the sets of results. Both methods are presented in the context of EA experimental research but can be applied more generally to data with two orthogonal group that can be combined to create a matrix of numeric data sets.