Finding Resilient Solutions for Dynamic Multi-Objective Constraint Optimization Problems

Abstract

Systems Resilience is a large-scale multi-disciplinary research that aims to identify general principles underlying the resilience of real world complex systems. Many conceptual frameworks have been proposed and discussed in the literature since Holling’s seminal paper (1973). Schwind et al. (2013) recently adopted a computational point of view of Systems Resilience, and modeled a resilient system as a dynamic constraint optimization problem. However, many real world optimization problems involve multiple criteria that should be considered separately and optimized simultaneously. Also, it is important to provide an algorithm that can evaluate the resilience of a dynamic system. In this paper, a framework for Dynamic Multi-Objective Constraint Optimization Problem (DMO-COP) is introduced and two solution criteria for solving this problem are provided, namely resistance and functionality, which are properties of interest underlying the resilience for DMO-COPs. Also, as an initial step toward developing an efficient algorithm for finding resilient solutions of a DMO-COP, an algorithm called Algorithm for Systems Resilience (ASR), which computes every resistant and functional solution for DMO-COPs, is presented and evaluated with different types of dynamical changes.