Abstract: This paper focuses on parameter search for multi-agent based models using evolutionary algorithms. Large numbers and variable dimensions of parameters require a search method which can efficiently handle a high dimensional search space. We are proposing the use of complexification as it emulates the natural way of evolution by starting with a small constrained search space and expanding it as the evolution progresses. We examined the effects of this method on an EA by evolving parameters for two multi-agent based models.

Abstract: An approach to business process modelling for short term KPI prediction, based on event logs and values of environment variables, is proposed. Ready-for-simulation process model is built semi-automatically, expert only inputs desired environment variables, which are used as features during the learning process. Process workflow is extracted as a Petri Net model using a combination of process mining algorithms. Dependencies between features and process variables are formalized using decision and regression trees techniques. Experiments were conducted to predict KPIs of real companies.

Abstract: The article presents a framework for interactive virtual environments’ development for simulation and modeling of complex systems. The framework uses system’s structural model as a core concept for composition and control of simulation-based scientific experiments not in terms of technological processes or workflows but in terms of domain-specific objects and their interconnection within the investigated system. The proposed framework enables integration and management of resources available within a cloud computing environment in order to support automatic simulation management and to provide the user with an interactive visual domain-specific interface to the system.

Abstract: This research outlines a new approach, that takes complex medical, nutritional & activity data and presents it to the diabetic patient in the form of a mobile app/game that uses interactive 3D computer graphics & game play to make this complex information more accessible. The pilot randomized control study results indicate that the Diabetes Visualizer’s use of interactive 3D game play increased the participants understanding of the condition, and its day-to-day management. More importantly the Diabetes Visualizer app stimulated participants interest in, and desire to engage in the task of diabetes management.

Abstract: Trajectory datasets have presented new opportunities for spatial computing applications and geo-informatics technologies with regard to emergency management. Existing research of trajectory analysis and data mining mainly employs algorithmic approaches and analyzing geometric information of trajectories. This study presents an efficient analytics tool based on visualization approaches for analyzing large volume of trajectory datasets. This approach is particular useful for emergency management when critical decisions based on semantic information are needed. Tourism trajectory datasets are used to demonstrate the proposed approach.

Abstract: The interactive scientific visualization applications are based on heterogeneous codes to implement simulation or data processing, visualization and interaction parts. These different parts need to be precisely assemble to construct an efficient application running in interactive time. Component-based approach is a good paradigm to express this kind of applications. The interactive scientific visualization domain is now classically extended with visual analysis applications. In this case, some parts of the application need to be added or removed dynamically during its execution. In this paper, we describe a component-based approach dedicated to dynamic interac- tive scientific visualization applications. We propose a framework called Modulight which implements our approach using the MPI2 library and the optimized socket library ØMQ. The performance of this framework is also analyzed from a real-life application of molecular dynamics.

Abstract: Acoustic recordings of the environment are an important aid to ecologists monitoring biodiversity and environmental health. However, rapid advances in recording technology, storage and computing make it possible to accumulate thousands of hours of recordings, of which, ecologists can only listen to a small fraction. The big-data challenge addressed in this paper is to visualize the content of long-duration audio recordings on multiple scales, from hours, days, months to years. The visualization should facilitate navigation and yield ecologically meaningful information. Our approach is to extract (at one minute resolution) acoustic indices which reflect content of ecological interest. An acoustic index is a statistic that summarizes some aspect of the distribution of acoustic energy in a recording. We combine indices to produce false-color images that reveal acoustic content and facilitate navigation through recordings that are months or even years in duration.

Abstract: Scientific models are often expressed as large and complicated programs. These programs embody numerous assumptions made by the developer (e.g. for differential equations, the discretization strategy and resolution). The complexity and pervasiveness of these assumptions means that often the only true description of the model is the software itself. This has led various researchers to call for scientists to publish their source code along with their papers. We argue that this is unlikely to be beneficial since it is almost impossible to separate implementation assumptions from the original scientific intent. Instead we advocate higher-level abstractions in programming languages, coupled with lightweight verification techniques such as specification and type systems. In this position paper, we suggest several novel techniques and outline an evolutionary approach to applying these to existing and future models. One-dimensional heat flow is used as an example throughout.

Abstract: A surrogate-based method for efficient multi-objective antenna optimization is presented. Our technique exploits response surface approximation (RSA) model constructed from sampled low-fidelity antenna model (here, obtained through coarse-discretization EM simulation). The RSA model enables fast determination of the best available trade-offs between conflicting design goals. A low-cost RSA model construction is possible through initial reduction of the design space. Optimization of the RSA model has been carried out using a multi-objective evolutionary algorithm (MOEA). Additional response correction techniques have been subsequently applied to improve selected designs at the high-fidelity EM antenna model level. The refined designs constitute the final Pareto set representation. The proposed approach has been validated using an ultra-wideband (UWB) monocone and a planar Yagi-Uda antenna.

Abstract: The second order Ordinary Differential Equation (ODE) system obtained after semidiscretizing the wave-type partial differential equation (PDE) with the finite element method (FEM) shows strong numerical stiffness. Its resolution requires the use of numerical methods with good stability properties and controlled numerical dissipation in the high-frequency range. The HHT-$\alpha$ and BDF-$\alpha$ methods are second order precision, unconditionally stable and able to dissipate high-modes for some values of the parameters. The finite element method has been applied to the one-dimensional linear wave-type PDE and to a non-linear version of a string of a guitar. The ODE systems obtained after applying FEM are solved by these two methods, proving that both are able to dissipate the high-modes.

Abstract: In this paper, we deal with problems of efficient resource management and scheduling in utility Grids. There are global job flows from external users along with resource owners’ local tasks upon resource non-dedication condition. Competition for resource reservation between independent users, local and global job flows substantially complicates scheduling and the requirement to provide the necessary quality of service. A meta-scheduling model, justified in this work, assumes a complex combination of job flow dispatching and application-level scheduling methods for jobs, as well as resource sharing and consumption policies established in virtual organizations (VOs) and based on economic principles. A solution to the problem of fair resource sharing among VO stakeholders with simulation studies is proposed.

Abstract: In this paper we propose a hybrid metaheuristic based on General Variable Neighbor- hood search and Integer Linear Programming for solving the vehicle routing problem with time windows (VRPTW).The problem consists in determining the minimum cost routes for a homogeneous fleet of vehicles to meet the demand of a set of customers within a specified time windows. The proposed heuristic, called VNS-SCP is considered as a matheuristic where the hybridization of heuristic (VNS) and exact (Set Covering Problem (SCP)) method is used in this approach as an intertwined collaborative cooperation manner. In this approach an initial solution is first created using Solomon route-construction heuristics, the nearest neighbor algorithm. In the second phase the solutions are improved in terms of the total distance traveled using VNS-SCP. The algorithm is tested using Solomon benchmark. Our findings indicate that the proposed procedure outperforms other local searches and metaheuristics.

Abstract: A robust simulation-driven methodology for rapid and reliable design of RF/microwave circuits comprising compact microstrip resonant cells (CMRCs) is presented. We introduce a nested space mapping (NSM) technology, in which the inner space mapping layer is utilized to improve the generalization capabilities of the equivalent circuit model corresponding to a constitutive element of the circuit under consideration. The outer layer enhances the surrogate model of the entire structure under design. We demonstrate that NSM significantly improves performance of surrogate-based optimization of composite RF/microwave structures. It is validated using two examples of UWB microstrip matching transformers (MTs) and compared to other competitive surrogate-assisted methods attempting to solve the problem of compact RF/microwave component design.

Abstract: Many optimisation problems are dynamic in the sense that changes occur during the optimisation process, and therefore are more challenging than the stationary problems. The occurrences of such problems have attracted researchers into studying areas of artificial intelligence and operational research. To solve dynamic optimisation problems, the proposed approaches should not only attempt to seek the global optima but be able to also keep track of changes in the track record of landscape solutions. Population-based approaches have been intensively investigated to address these problems, as solutions are scattered over the entire search space and therefore helps in recognizing any changes that occur in the search space but however, optimisation algorithms that have been used to solve stationary problems cannot be directly applied to handle dynamic problems without any modifications such as in maintaining population diversity. In this research work, one of the most recent new population-based meta-heuristic optimisation technique called a harmony search algorithm for dynamic optimization problems is investigated. This technique mimics the musical process when a musician attempts to find a state of harmony. In order to cope with a dynamic behaviour, the proposed harmony search algorithm was hybridised with a (i) random immigrant, (ii) memory mechanism and (iii) memory based immigrant scheme. This hybridisation processes help to keep track of the changes and to maintain the population diversity. The performance of the proposed harmony search is verified by using the well-known dynamic test problem called the Moving Peak Benchmark (MPB) with a variety of peaks. The empirical results demonstrate that the proposed algorithm is able to obtain competitive results, but not the best for most of the cases, when compared to the best known results in the scientific literature published so far.

Abstract: Contemporary motor vehicles have increasing numbers of automated functions to augment the safety and comfort of a car. The automotive industry has to incorporate increasing numbers of processing units in the structure of cars to run the software that provides these functionalities. The software components often need access to sensors or mechanical devices which they are designed to operate. The result is a network of hardware units which can accommodate a limited number of software programs, each of which has to be assigned to a hardware unit. A prime goal of this deployment problem is to nd softwareto-hardware assignments that maximise the reliability of the system. In doing so, the assignments have to observe a number of constraints to be viable. This includes limited memory of a hardware unit, collocation of software components on the same hardware units, and communication between software components. Since the problem consists of many constraints with a signicantly large search space, we investigate an ACO and constraint programming (CP) hybrid for this problem. We nd that despite the large number of constraints, ACO on its own is the most eective method providing good solutions by also exploring infeasible regions.

Abstract: A major factor in the consideration of an electrical power network of the scale of a national grid is the calculation of power flow and in particular, optimal power flow. This paper considers such a network, in which distributed generation is used, and examines how the network can be optimized, in terms of transmission line capacity, in order to obtain optimal or at least high-performing configurations, using multi-objective optimisation by evolutionary computing methods.

Abstract: Geostatistical methods provide a powerful tool to understand the complexity of data arising from Earth sciences. Since the mid 70’s, this numerical approach is widely used to understand the spatial variation of natural phenomena in various domains like Oil and Gas, Mining or Environmental Industries. Considering the huge amount of data available, standard imple- mentations of these numerical methods are not efficient enough to tackle current challenges in geosciences. Moreover, most of the software packages available for geostatisticians are de- signed for a usage on a desktop computer due to the trial and error procedure used during the interpolation. The Geological Data Management (GDM ) software package developed by the French geological survey (BRGM) is widely used to build reliable three-dimensional geological models that require a large amount of memory and computing resources. Considering the most time-consuming phase of kriging methodology, we introduce an efficient out-of-core algorithm that fully benefits from graphics cards acceleration on desktop computer. This way we are able to accelerate kriging on GPU with data 4 times bigger than a classical in-core GPU algorithm, with a limited loss of performances.

Abstract: With the advanced image, drilling and field observation technology, geological structure of reservoirs can be described in more details. A novel 3D mesh generation method for meshing reservoir models is proposed and implemented with arbitrary stratigraphical surface constraints, which ensures the detailed structure geometries and material properties of reservoirs are better described and analysed. The stratigraphic interfaces are firstly extracted and meshed, and then a tetrahedron mesh is generated in 3D with the constraints of such meshed surfaces. The proposed approach includes the following five steps: (1) extracting stratum interfaces; (2) creating a background mesh with size field on the interfaces; (3) constructing geodesic isolines from interface boundaries to the interior; (4) employing a geodesic-based approach to create surface triangles on the area between adjacent isolines and then merge them together; (5) generating tetrahedron mesh for 3D reservoirs with constraints of generated surface triangular mesh. The proposed approach has been implemented and applied to the Lawn Hill reservoir as a practical example to demonstrate its effectiveness and usefulness.

Abstract: We are developing a coupling software ppOpen-MATH/MP. ppOpen-MATH/MP is characterized by its wide applicability. This feature comes from the design that grid point correspondence and interpolation coefficients should be calculated in advance. However, calculation of these values on the unstructured grid model requires a lot of computation time in general. So, we developed new effective algorithm and program for calculating the grid point correspondence as a pre-processor of ppOpen-MATH/MP. In this article, an algorithm and performance evaluation of the program is presented in the first half, and next, an application example of ppOpen-MATH/MP, targeting atmospheric model NICAM and ocean model COCO coupling, is described.

Abstract: In order to execute various finite-difference method applications on large-scale parallel computers with a reasonable cost of computer resources, a framework using an adaptive mesh refinement (AMR) technique has been developed. AMR can realize high-resolution simulations while saving computer resources by generating and removing hierarchical grids dynamically. In the AMR framework, a dynamic domain decomposition (DDD) technique, as a dynamic load balancing method, is also implemented to correct the computational load imbalance between each process associated with parallelization. By performing a 3D AMR test simulation, it is confirmed that dynamic load balancing can be achieved and execution time can be reduced by introducing the DDD technique.

Abstract: We present an overview of a software system, Bellerophon, built to support a production-level HPC application called CHIMERA, which simulates core-collapse supernova events at the petascale. Developed over the last four years, Bellerophon enables CHIMERA’s geographically dispersed team of collaborators to perform data analysis in near real-time. Its n-tier architecture provides an encapsulated, end-to-end software solution that enables the CHIMERA team to quickly and easily access highly customizable animated and static views of results from anywhere in the world via a web-deliverable, cross-platform desktop application. In addition, Bellerophon addresses software engineering tasks for the CHIMERA team by providing an automated mechanism for performing regression testing on a variety of supercomputing platforms. Elements of the team’s workflow management needs are met with software tools that dynamically generate code repository statistics, access important online resources, and monitor the current status of several supercomputing resources.

Abstract: One key factor in the improved understanding of earth system science is the development and improvement of high fidelity earth system models. Along with the deeper understanding of system processes, the complexity of software systems of those modelling systems becomes a barrier for further rapid model improvements and validation. In this paper, we present our experience on better understanding the Community Land Model (CLM) within an earth system modelling framework. First, we give an overview of the software system of the global offline CLM system. Second, we present our approach to better understand the CLM software structure and data structure using advanced software tools. After that, we focus on the practical issues related to CLM computational performance and individual ecosystem function. Since better software engineering practices are much needed for general scientific software systems, we hope those considerations can be beneficial to many other modeling research programs involving multiscale system dynamics.

Abstract: Static software analyser tools can help in program comprehension by detecting relations among program parts. Detecting relations among the concurrent program parts, e.g. relations between processes, is not straightforward. In case of dynamic languages only a (good) approximation of the real dependencies can be calculated. In this paper we present algorithms to build a process relation graph for Erlang programs. The graph contains direct relation through message passing and hidden relations represented by the ETS tables.