An investigation of the efficient implementation of Cellular Automata on multi-core CPU and GPU hardware

Copyright © 2015 Elsevier. NOTICE: this is the author’s version of a work that was accepted for publication in Journal of Parallel and Distributed Computing . Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Parallel and Distributed Computing Vol. 77 (2015), DOI: 10.1016/j.jpdc.2014.10.011Cellular automata (CA) have proven to be excellent tools for the simulation of a wide variety of phenomena in the natural world. They are ideal candidates for acceleration with modern general purpose-graphical processing units (GPU/GPGPU) hardware that consists of large numbers of small, tightly-coupled processors. In this study the potential for speeding up CA execution using multi-core CPUs and GPUs is investigated and the scalability of doing so with respect to standard CA parameters such as lattice and neighbourhood sizes, number of states and generations is determined. Additionally the impact of ‘Activity’ (the number of ‘alive’ cells) within a given CA simulation is investigated in terms of both varying the random initial distribution levels of ‘alive’ cells, and via the use of novel state transition rules; where a change in the dynamics of these rules (i.e. the number of states) allows for the investigation of the variable complexity within.Engineering and Physical Sciences Research Council (EPSRC

Pipe smoothing genetic algorithm for least cost water distribution network design

This is the author accepted manuscript. The final version is available from the publisher via the DOI in this record.GECCO '13 Proceedings of the 15th annual conference on Genetic and evolutionary computation Amsterdam, Netherlands — July 06 - 10, 2013This paper describes the development of a Pipe Smoothing Genetic Algorithm (PSGA) and its application to the problem of least cost water distribution network design. Genetic algorithms have been used widely for the optimisation of both theoretical and real-world non-linear optimisation problems, including water system design and maintenance problems. In this work we propose a pipe smoothing based approach to the creation and mutation of chromosomes which utilises engineering expertise with the view to increasing the performance of the algorithm compared to a standard genetic algorithm. Both PSGA and the standard genetic algorithm were tested on benchmark water distribution networks from the literature. In all cases PSGA achieves higher optimality in fewer solution evaluations than the standard genetic algorithm

Understanding the efficient parallelisation of Cellular Automata on CPU and GPGPU hardware

Cellular automata, represented by a discrete set of elements are ideal candidates for parallelisation, particularly on graphics cards using GPGPU technology. This paper shows that the speedups of 50 times over CPU are possible but that the hardware is only partially responsible and the memory model is vital to exploiting the additional computational power of the GPU

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

Data-driven study of discolouration material mobilisation in trunk mains

This is the final version of the article. Available from the publisher via the DOI in this record.It has been shown that sufficiently high velocities can cause the mobilisation of discolouration material in water distribution systems. However, how much typical hydraulic conditions affect the mobilisation of discolouration material has yet to be thoroughly investigated. In this paper, results are presented from real turbidity and flow observations collected from three U.K. trunk main networks over a period of two years and 11 months. A methodology is presented that determines whether discolouration material has been mobilised by hydraulic forces and the origin of that material. The methodology found that the majority of turbidity observations over 1 Nephelometric Turbidity Units (NTU) could be linked to a preceding hydraulic force that exceeded an upstream pipe’s hydraulically preconditioned state. The findings presented in this paper show the potential in proactively managing the hydraulic profile to reduce discolouration risk and improve customer service.The authors are grateful to the Engineering and Physical Sciences Research Council (EPSRC) for providing the financial support as part of the STREAM project and to Julian Collingbourne of South West Water for supplying the data used in this paper

Adaptive Locally Constrained Genetic Algorithm For Least-Cost Water Distribution Network Design

Copyright © IWA Publishing 2014. The definitive peer-reviewed and edited version of this article is published in Journal of Hydroinformatics Vol.16 (2), pp. 288–301 (2014), DOI: 10.2166/hydro.2013.218 and is available at www.iwapublishing.comThis paper describes the development of an adaptive locally constrained genetic algorithm (ALCO-GA) and its application to the problem of least cost water distribution network design. Genetic algorithms have been used widely for the optimisation of both theoretical and real-world nonlinear optimisation problems, including water system design and maintenance problems. In this work we propose a heuristic-based approach to the mutation of chromosomes with the algorithm employing an adaptive mutation operator which utilises hydraulic head information and an elementary heuristic to increase the efficiency of the algorithm's search into the feasible solution space. In almost all test instances ALCO-GA displays faster convergence and reaches the feasible solution space faster than the standard genetic algorithm. ALCO-GA also achieves high optimality when compared to solutions from the literature and often obtains better solutions than the standard genetic algorithm

Continuous Trait-Based Particle Swarm Optimisation (CTB-PSO)

Copyright © 2012 Springer Verlag. The final publication is available at link.springer.com8th International Conference, ANTS 2012, Brussels, Belgium, September 12-14, 2012. ProceedingsIn natural flocks, individuals are often of the same species, but there exists considerable variation in the traits possessed by each individual. In much the same way as humans display varied levels of aggression, gregariousness and inquisitiveness, so do the animals on which PSO is based [1]. Recent research has shown that this disparity of behaviour is very important in the ability of the flock to solve problems effectively, which might have profound implications for PSO. One of the key aspects is that although certain behaviour types (e.g. more adventurous individuals) might individually be better at problem solving; selecting for a group that all have adventurous traits has been shown to reduce the performance of the flock as a whole [1]. Therefore a flock that has a variety of behaviours leads to better performance in natural systems and it is this that motivates the work here. This paper explores a variant of PSO known as Continuous Trait-Based PSO (CTB-PSO) where individuals within a swarm have traits based on a continuous scale as opposed to discrete behaviour groupings

Early Warning System for Bathing Water Quality

Poster describing use of Artificial Neural Networks (ANN) in a Receiver-Operating Characteristic (ROC) scenario to predict bathing water quality exceedances at beaches in the SW UK.Environment Agency (SW

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

Genetic Programming For Cellular Automata Urban Inundation Modelling

Recent advances in Cellular Automata (CA) represent a new, computationally efficient method of simulating flooding in urban areas. A number of recent publications in this field have shown that CAs can be much more computationally efficient than methods than using standard shallow water equations (Saint Venant/Navier-Stokes equations). CAs operate using local state-transition rules that determine the progression of the flow from one cell in the grid to another cell, in many publications the Manning’s Formula is used as a simplified local state transition rule. Through the distributed interactions of the CA, computationally simplified urban flooding can be processed, although these methods are limited by the approximation represented by the Manning’s formula. Literature demonstrates that the viability of the Manning’s formula will break down with too large a time step, flow rates, too small a cell size, or too smooth roughness factor; Therefore further increases in computational efficiency could be gained with a better approximation, or rather one capable of producing the required simulation with enough accuracy at larger time steps, smaller cells sizes, smoother roughness factors. Genetic programming has the potential to be used to optimise state transition rules to maximise accuracy and minimise computation time. In this paper we present some preliminary findings on the use of genetic programming (GP) for deriving these rules automatically. The experimentation compares GP-derived rules with human created solutions based on the Mannings formula and findings indicate that the GP rules can improve on these approaches