G. Narzisi, V. Mysore, and B. Mishra (USA)
Multi-Objective Optimization, Agent-based Modeling,
Pareto Front, Multi-Objective Evolutionary Algorithms,
Robustness, Disaster Management.
Agent-based models (ABMs) / multi-agent systems
(MASs) are today one of the most widely used modeling–
simulation–analysis approaches for understanding the dy
namical behavior of complex systems. These models are
often characterized by several parameters with nonlinear
interactions which together determine the global system
dynamics, usually measured by different conﬂicting crite
ria. The problem that emerges is that of tuning the control
lable system parameters at the local level, in order to reach
some desirable global behavior.
In this research paper, we cast the tuning of an ABM
for emergency response planning as a multi-objective op
timization problem (MOOP). We then propose the use of
multi-objective evolutionary algorithms (MOEAs) for ex
ploration and optimization of the resultant search space.
We employ two well-known MOEAs, the Nondominated
Sorting Genetic Algorithm II (NSGA-II) and the Pareto
Archived Evolution Strategy (PAES), and test their perfor
mance for different pairs of objectives for plan evaluation.
In the experimental results, the approximate Pareto front of
the non-dominated solutions is effectively obtained. Fur
ther, a conﬂict between the proposed objectives is patent.
Additional robustness analysis is performed to help policy
makers select a plan according to higher-level information
or criteria not present in the original problem description.