Flocks, herds and schools: A distributed behavioral model
SIGGRAPH '87 Proceedings of the 14th annual conference on Computer graphics and interactive techniques
Discrete-event simulation and the event horizon
PADS '94 Proceedings of the eighth workshop on Parallel and distributed simulation
Cognition and affect (poster): architectures and tools
AGENTS '98 Proceedings of the second international conference on Autonomous agents
Model structure and load balancing in optimistic parallel discrete event simulation
PADS '00 Proceedings of the fourteenth workshop on Parallel and distributed simulation
MACE3J: fast flexible distributed simulation of large, large-grain multi-agent systems
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 2
ICAL 2003 Proceedings of the eighth international conference on Artificial life
IAT '03 Proceedings of the IEEE/WIC International Conference on Intelligent Agent Technology
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Resolving mutually exclusive interactions in agent based distributed simulations
WSC '04 Proceedings of the 36th conference on Winter simulation
Web Intelligence and Agent Systems
SpringSim '09 Proceedings of the 2009 Spring Simulation Multiconference
A reference model for agent-based modeling and simulation
SpringSim '09 Proceedings of the 2009 Spring Simulation Multiconference
Topological effects on the performance of island model of parallel genetic algorithm
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advences in computational intelligence - Volume Part II
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We propose a framework for defining agent-based models (ABMs) and two algorithms for the automatic parallelization of agent-based models, a general version P-ABMG for all ABMs definable in the framework and a more specific variant P-ABMS for "spatial ABMs" targeted at SWARM and ANT-based models, where the additional spatial information can be utilized to obtain performance improvements. Both algorithms can automatically distribute ABMs over multiple CPUs and dynamically adjust the degree of parallelization based on available computational resources throughout the simulation runs. We also describe a first implementation of P-ABMS in our SWAGES environment and report both results from simulations with simple SWARM agents that provide a lower bound for the performance gains achievable by the algorithm and results from simulations with more complex deliberative agents, which need to synchronize their state after each update cycle. Even in the latter case, we show that in some conditions the algorithm is able to achieve close-to-maximum performance gains.