Large-scale agent simulation of agent-based models

• Main Author: Poh, Wei Li.
• Other Authors: School of Computer Engineering
• Format: Final Year Project
• Language: English
• Published: 2012
• Subjects: DRNTU::Engineering::Computer science and engineering::Computing methodologies::Simulation and modeling
• Online Access: http://hdl.handle.net/10356/48602
• Institution: Nanyang Technological University

Agents are a very useful form of modelling and understanding complex natural systems. Agent-based simulations have been applied in many different domains, such as: economics, sociology, psychology, etc. While they offer a very flexible approach to modeling such systems, they often require significant computational resources for execution. Usually, distributed computing techniques are widely used in the simulation and modeling of agents in order to reduce simulation or computational time. There are mainly 3 factors that affect computational time: node-to-node communication cost, migration cost of agents and the load of each node. Depending on the type of partition methods used, it would affect the nodes communication cost and the node’s load in which may impact to the simulation time. This project will investigate the effectiveness of different partition methods for distributed simulation of agent-based models (ABM) in discrete and continuous environments. The intention is to provide faster execution of the models. Case studies example used for discrete environment is Game of Life model (GoL) and Boids model for continuous environment. The results find that static partitioning of environment horizontally (SPH) provided the fastest execution time for discrete environment. For continuous environment, depending on the behavior of the agents, static partitioning of environment vertically (SPV) or grid-based partitioning may provide the fastest execution time.