Abstract: Climate change as a result of human activities is a problem of a paramount importance. The global temperature on Earth is gradually increasing and it may lead to substantially hotter summers in a moderate belt of Europe, which in turn is likely to influence the air conditioning penetration in this region. The current work is an attempt to predict air conditioning penetration in different residential areas in the UK between 2030-2090 using an integration of calibrated building models, future weather predictions and an agent-based model. Simulation results suggest that up to 12% of homes would install an air conditioner in 75 years’ time assuming an average purchasing ability of the households. The performed simulations provide more insight into the influence of overheating intensity along with households’ purchasing ability and social norms upon households’ decisions to purchase an air conditioner.

Abstract: Crowd evacuations involve thousands of persons in closed spaces. Having knowledge about where the problematic exits will be or where the disaster may occur can be crucial in emergency planning. We implemented a simulator using Agent Based Modelling able to model the behaviour of people in evacuation situations and a workflow able to run it in the cloud. The input is just a PNG image and the output are statistical results of the simulation executed on the cloud. This allows to provide the user with a system abstraction and only a map of the scenario is needed. Many events are held in main city squares, so to test our system we chose Siena and we fit about 28,000 individuals in the centre of the square. The software has special computational requirements because the results need to be statistically reliable. Because these needs we use distributed computing. In this paper we show how the simulator scales efficiently on the cloud.

Abstract: Evolutionary algorithms (EAs) are popular and powerful approaches for model calibration. This paper proposes an enhanced EA-based model calibration method, namely the differential evolution (DE) with sensitivity analysis and the Powell's method (DESAP). In contrast to traditional EA-based model calibration methods, the proposed DESAP owns three main features. First, an entropy-based sensitivity analysis operation is integrated so as to dynamically identify important parameters of the model as evolution progresses online. Second, the Powell's method is performed periodically to fine-tune the important parameters of the best individual in the population. Finally, in each generation, the DE operators are performed on a small number of better individuals rather than all individuals in the population. These new search mechanisms are integrated into the DE framework so as to reduce the computational cost and to improve the search efficiency. To validate its effectiveness, the proposed DESAP is applied to two crowd model calibration cases. The results demonstrate that the proposed DESAP outperforms several state-of-the-art model calibration methods in terms of accuracy and efficiency.

Abstract: Data partitioning is one of the main problems in parallel and distributed simulation. Distribution of data over the architecture directly influences the efficiency of the simulation. The partitioning strategy becomes a complex problem because it depends on several factors. In an Individual-oriented Model, for example, the partitioning is related to interactions between the individual and the environment. Therefore, parallel and distributed simulation should dynamically enable the interchange of the partitioning strategy in order to choose the most appropriate partitioning strategy for a specific context. In this paper, we propose a strip partitioning strategy to a spatially dependent problem in Individual-oriented Model applications. This strategy avoids sharing resources, and, as a result, it decreases communication volume among the processes. In addition, we develop an objective function that calculates the best partitioning for a specific configuration and gives the computing cost of each partition, allowing for a computing balance through a mapping policy. The results obtained are supported by statistical analysis and experimentation with an Ant Colony application. As a main contribution, we developed a solution where the partitioning strategy can be chosen dynamically and always returns the lowest total execution time.

Abstract: In recent years, thanks to the low cost of deploying, maintaining an Unmanned Aerial Vehicle (UAV) system and the possibility to operating them in areas inaccessible or dangerous for human pilots, UAVs have attracted much research attention both in the military field and civilian application. In order to deal with more sophisticated tasks, such as searching survival points, multiple target monitoring and tracking, the application of UAV swarms is forseen. This requires more complex control, communication and coordination mechanisms. However, these mechanisms are difficult to test and analyze under flight dynamic conditions. These multi- UAV scenarios are by their nature well suited to be modeled and simulated as multi-agent systems. The first step of modeling an multi-agent system is to construct the model of agent, namely accurate model to represent its behavior, constraints and uncertainties of UAVs. In this paper we introduce our approach to model an UAV as an agent in terms of multi-agent system principle. Construction of the model to satisfy the need for a simulation environment that researchers can use to evaluate and analyze swarm control mechanisms. Simulations results of a case study is provided to demonstrate one possible use of this approach.