The ant colony optimization meta-heuristic
New ideas in optimization
Ant algorithms for discrete optimization
Artificial Life
Characterizing Web Usage Regularities with Information Foraging Agents
IEEE Transactions on Knowledge and Data Engineering
Autonomy Oriented Computing: From Problem Solving to Complex Systems Modeling (Multiagent Systems, Artificial Societies, and Simulated Organizations)
Toward nature-inspired computing
Communications of the ACM
An enhanced massively multi-agent system for discovering HIV population dynamics
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part II
AOC-by-self-discovery modeling and simulation for HIV
LSMS'07 Proceedings of the 2007 international conference on Life System Modeling and Simulation
Multi-agent model of hepatitis C virus infection
Artificial Intelligence in Medicine
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In MMAS-based biological system simulation, it is a challenging task to deal with numerous interactions among a vast number of autonomous agents. In our work, a hybrid massively multi-agent systems (MMAS) model is developed, and it incorporates the characteristics of cellular automaton (CA) and system-level mathematical equation modeling to simulate HIV-immune interaction dynamics. The mathematical equations are adopted within the site of a two-dimensional lattice. As the average high density, agent interactions can be calculated according to the equations without significantly affecting the performance of the systems studied. In the mean time, the CA model keeps the spatial characteristics of HIV evolution among the sites. The simulation based on the implemented MMAS discovers the dynamics of HIV evolution over different temporal and spatial scales, and reproduces the typical three-stage dynamics of HIV infection.