TY - GEN
T1 - Multi-objective evolutionary optimization of agent-based models
T2 - 2nd IASTED International Conference on Computational Intelligence, CI 2006
AU - Narzisi, Giuseppe
AU - Mysore, Venkatesh
AU - Mishra, Bud
PY - 2006
Y1 - 2006
N2 - Agent-based models (ABMs) / multi-agent systems (MASs) are today one of the most widely used modeling-simulation-analysis approaches for understanding the dynamical 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 criteria. The problem that emerges is that of tuning the controllable 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 optimization problem (MOOP). We then propose the use of multi-objective evolutionary algorithms (MOEAs) for exploration 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 performance 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 is patent. Additional robustness analysis is performed to help policymakers select a plan according to higher-level information or criteria not present in the original problem description.
AB - Agent-based models (ABMs) / multi-agent systems (MASs) are today one of the most widely used modeling-simulation-analysis approaches for understanding the dynamical 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 criteria. The problem that emerges is that of tuning the controllable 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 optimization problem (MOOP). We then propose the use of multi-objective evolutionary algorithms (MOEAs) for exploration 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 performance 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 is patent. Additional robustness analysis is performed to help policymakers select a plan according to higher-level information or criteria not present in the original problem description.
KW - Agent-based modeling
KW - Disaster management
KW - Multi-objective evolutionary algorithms
KW - Multi-objective optimization
KW - Pareto front
KW - Robustness
UR - http://www.scopus.com/inward/record.url?scp=37249006004&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=37249006004&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:37249006004
SN - 0889866023
SN - 9780889866027
T3 - Proceedings of the 2nd IASTED International Conference on Computational Intelligence, CI 2006
SP - 224
EP - 230
BT - Proceedings of the 2nd IASTED International Conference on Computational Intelligence, CI 2006
Y2 - 20 November 2006 through 22 November 2006
ER -