US 8,015,127 B2 · System, method, and computer-accessible medium for providing a multi-objective evolutionary optimization of agent-based models

Abstract

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.

Claims26

Dependent claims

A non-transitory computer-readable medium for a multi-objective optimization of a complex dynamical system which includes thereon a set of instructions, wherein the set of instructions is configured to effectuate a processing arrangement to perform procedures comprising:

(a) receiving a plurality of agents configured to exhibit stochastic behavior, a plurality of parameters and constraints associated with the system, a set of variables associated with the system, and a plurality of objective functions associated with the system;
(b) determining at least one behavior of the system based on the variables, the parameters, and the constraints by applying at least one multi-objective evolutionary procedure to the agents, wherein the stochastic behavior of the agents is used to generate at least one Pareto front associated with the system through multiple simulations, wherein determination of optimization includes evaluation of robustness of the at least one Pareto front;
(c) independently and concurrently optimizing two or more of the objective functions using the at least one multi-objective evolutionary procedure;
and
(d) selecting information of the system variables associated with at least one member of the Pareto front based on the objective functions.

2 more elements

A system for multi-objective optimization of a complex dynamical system, comprising:

a processing arrangement;
and
a computer-readable medium which includes thereon a set of instructions, wherein the set of instructions is configured to effectuate a processing arrangement to perform procedures comprising:
(a) receiving a plurality of agents configured to exhibit stochastic behavior, a plurality of parameters and constraints associated with the system, a set of variables associated with the system, and a plurality of objective functions associated with the system;
(b) determining at least one behavior of the system based on the variables, the parameters, and the constraints by applying at least one multi-objective evolutionary procedure to the agents, wherein the stochastic behavior of the agents is used to generate at least one Pareto front associated with the system through multiple simulations, wherein determination of optimization includes evaluation of robustness of the at least one Pareto front;
(c) independently and concurrently optimizing two or more of the objective functions using the at least one multi-objective evolutionary procedure;
and
(d) selecting information of the system variables associated with at least one member of the Pareto front based on the objective functions.

5 more elements

Dependencies are inferred from the claim text.

System, method, and computer-accessible medium for providing a multi-objective evolutionary optimization of agent-based models

Life of this patent

System, method, and computer-accessible medium for providing a multi-objective evolutionary optimization of agent-based models

Life of this patent

Abstract

Claims26