Progressive Surrogate Modeling for a Multi-objective Competitive Co-evolutionary (MoCCoEv) Wargame Strategy Optimization

Wargame strategy optimization involves two competing agents—offense and defense—and requires extensive simulations of wargame tools, resulting in high computational costs for evaluating potential strategies. To reduce these costs, surrogate models are employed to approximate the objective functions, with their accuracy directly affecting the optimization process. This paper proposes a novel framework for progressive surrogate modeling to improve the quality of surrogate models while addressing the wargame strategy optimization problem. Our approach utilizes a Multi-objective Competitive Co-Evolutionary (MoCCoEv) algorithm, which iteratively refines the surrogate models. The process begins by using MoCCoEv algorithm to optimize wargame strategies generating new strategies. The new strategies are then replaced in the training data to update and improve the surrogate models. This cyclical process ensures that the models are continuously refined in the regions of interest, leading to more accurate predictions and enhanced optimization results.