Multiobjective Competitive Co-Evolutionary Optimization and Regularity-Based Decision-Making for Two-Agent Wargame Strategy Optimization
针对兵棋推演中攻防双方策略相互依赖且各自有多重冲突目标的问题，提出多目标竞争协同进化框架，结合规律性搜索与交互式决策，支持渐进收缩策略参数维度，适用于多智能体优化。
Many practical problems involve multiple interdependent agents, each aiming to optimize its own objectives. Wargame strategy optimization, which requires optimizing strategies for at least two agents—attackers and defenders—presents unique challenges due to the interdependence of the agents’ strategies. This characteristic necessitates a co-evolutionary approach, where each agent’s strategy is continually adjusted in response to the other’s. The complexity increases when each agent pursues multiple conflicting objectives, resulting in Pareto-optimal strategy sets that require sequential decision-making (DM). To address these challenges, we introduce a novel multi-objective competitive co-evolutionary optimization (MoCCoEv) framework, specifically tailored for wargame strategy optimization. This framework integrates regularity-based search with an iterative and interactive DM approach, fostering a continuous interplay between co-evolving agents. Additionally, we introduce the concept of progressive shrinking, which interactively reduces the dimensions of the agents’ strategy parameters to mirror real-world decision-making by enforcing commitment to earlier moves and facilitating effective strategic choices. Our flexible and adaptable framework also supports alternative strategies, such as deception, and can be applied to other multi-agent optimization problems.