Coalition formation has been a fundamental form of resource cooperation for achieving joint goals in multiagent systems. Most existing studies still focus on the traditional assumption that an agent has to contribute its resources to all the goals, even if the agent is not interested in the goal at all. In this article, a natural extension of the traditional coalitional resource games (CRGs) is studied from both theoretical and empirical perspectives, in which each agent has uncompromising, personalized preferences over goals. Specifically, a new CRGs model with agents’ strict preferences for goals is presented, in which an agent is willing to contribute its resources only to the goals that are in its own interest set. The computational complexity of the basic decision problems surrounding the successful coalition is reinvestigated. The results suggest that these problems in such a strict preference way are complex and intractable. To find the largest successful coalition for possible computation reduction or potential parallel processing, a flow-network–based exhaust algorithm, called FNetEA, is proposed to achieve the optimal solution. Then, to solve the problem more efficiently, a hybrid algorithm, named 2D-HA, is developed to find the approximately optimal solution on the basis of genetic algorithm, two-dimensional (2D) solution representation, and a heuristic for solution repairs. Through extensive experiments, the 2D-HA algorithm exhibits the prominent ability to provide reassurances that the optimal solution could be found within a reasonable period of time, even in a super-large-scale space.

中文翻译：

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在代理人的严格目标偏好下寻找最大的成功联盟

联盟的形成一直是资源合作的一种基本形式，用于在多智能体系统中实现共同目标。大多数现有研究仍然集中在传统假设上，即代理人必须为所有目标贡献其资源，即使代理人根本对目标不感兴趣。在本文中，从理论和经验的角度研究了传统联合资源博弈 (CRG) 的自然扩展，其中每个代理对目标都有不妥协的、个性化的偏好。具体来说，提出了一种新的 CRGs 模型，其中代理人对目标有严格的偏好，其中代理人愿意仅将其资源贡献给符合其自身利益的目标。重新研究围绕成功联盟的基本决策问题的计算复杂性。结果表明，以这种严格的偏好方式出现的这些问题是复杂且难以解决的。为了找到可能的计算减少或潜在的并行处理的最大成功联盟，提出了一种基于流网络的排气算法，称为 FNetEA，以实现最佳解决方案。然后，为了更有效地解决问题，开发了一种名为 2D-HA 的混合算法，以在遗传算法、二维 (2D) 解表示和解修复启发式算法的基础上找到近似最优解。通过大量实验，2D-HA 算法表现出突出的能力，可以保证即使在超大规模空间中也可以在合理的时间内找到最优解。