Solving the inverse graph model for conflict resolution using a hybrid metaheuristic algorithm

Abstract

The paper is concerned with one of the most important questions in conflict analysis: How decision makers can strategically interact in a conflict to reach a specified equilibrium? More specifically, an inverse preference optimization model is formulated to solve the problem of adjusting the preferences of decision makers so as to make a specified state an equilibrium. To this end we define an algorithm which incorporates the hybridization of particle swarm optimization and genetic algorithm. The proposed algorithm adopts a random neighbor strategy for population initialization, the elite reservation and mixed selection operation, and a diversity strategy for population update to improve its efficiency and effectiveness when searching for the required preferences. This approach can help decision makers or third parties to focus their resources on guiding them toward preferences that lead to a specified resolution. Finally, a real-world dispute over exporting bulk water from Eastern Canada is used to demonstrate the applicability and effectiveness of the approach.