Multi-objective evolutionary optimization of agent-based models: An application to emergency response planning

Giuseppe Narzisi, Venkatesh Mysore, Bud Mishra

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish (US)
Title of host publicationProceedings of the 2nd IASTED International Conference on Computational Intelligence, CI 2006
Pages224-230
Number of pages7
StatePublished - 2006
Event2nd IASTED International Conference on Computational Intelligence, CI 2006 - San Francisco, CA, United States
Duration: Nov 20 2006Nov 22 2006

Publication series

NameProceedings of the 2nd IASTED International Conference on Computational Intelligence, CI 2006

Other

Other2nd IASTED International Conference on Computational Intelligence, CI 2006
CountryUnited States
CitySan Francisco, CA
Period11/20/0611/22/06

Keywords

  • Agent-based modeling
  • Disaster management
  • Multi-objective evolutionary algorithms
  • Multi-objective optimization
  • Pareto front
  • Robustness

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computational Mechanics

Fingerprint Dive into the research topics of 'Multi-objective evolutionary optimization of agent-based models: An application to emergency response planning'. Together they form a unique fingerprint.

  • Cite this

    Narzisi, G., Mysore, V., & Mishra, B. (2006). Multi-objective evolutionary optimization of agent-based models: An application to emergency response planning. In Proceedings of the 2nd IASTED International Conference on Computational Intelligence, CI 2006 (pp. 224-230). (Proceedings of the 2nd IASTED International Conference on Computational Intelligence, CI 2006).