Agent-based models (ABMs) / multi-agent systems (
MASs) are one of the most widely used modeling-
simulation-analysis approaches for understanding the dynamical behavior of complex systems. These models can be often characterized by several parameters with nonlinear interactions which together determine the
global system dynamics, usually measured by different conflicting criteria. One problem that can emerge is that of tuning the controllable
system parameters at the local level, in order to reach some desirable global behavior. According to one exemplary embodiment t of the present invention, the tuning of an ABM for
emergency response planning can be cast as a multi-objective
optimization problem (MOOP). Further, the use of multi-objective evolutionary algorithms (MOEAs) and procedures for exploration and optimization of the
resultant search space can be utilized. It is possible to employ conventional MOEAs, e.g., the Nondominated Sorting
Genetic Algorithm II (NSGA-II) and the Pareto Archived
Evolution Strategy (PAES), and their performance can be tested for different pairs of objectives for
plan evaluation. In the experimental results, the approximate Pareto front of the non-dominated solutions is effectively obtained. Further, a conflict between the proposed objectives can be seen. Additional robustness analysis may be performed to assist policy-makers in selecting a plan according to higher-level information or criteria which is likely not present in the original
problem description.