Evolutionary optimization is a generic population-based metaheuristic that can be adapted to solve a wide variety of optimization problems and has proven very effective for combinatorial optimization problems. However, the potential of this metaheuristic has not been utilized in Distributed Constraint Optimization Problems (DCOPs), a well-known class of combinatorial optimization problems prevalent in Multi-Agent Systems. In this paper, we present a novel population-based algorithm, Anytime Evolutionary DCOP (AED), that uses evolutionary optimization to solve DCOPs. In AED, the agents cooperatively construct an initial set of random solutions and gradually improve them through a new mechanism that considers an optimistic approximation of local benefits. Moreover, we present a new anytime update mechanism for AED that identifies the best among a distributed set of candidate solutions and notifies all the agents when a new best is found. In our theoretical analysis, we prove that AED is anytime. Finally, we present empirical results indicating AED outperforms the state-of-the-art DCOP algorithms in terms of solution quality.

中文翻译：

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AED：随时进化的 DCOP 算法

进化优化是一种通用的基于群体的元启发式算法，可适用于解决各种优化问题，并已证明对组合优化问题非常有效。然而，这种元启发式的潜力尚未在分布式约束优化问题 (DCOP) 中得到利用，这是多代理系统中普遍存在的一类众所周知的组合优化问题。在本文中，我们提出了一种新的基于种群的算法，即 Anytime Evolutionary DCOP (AED)，它使用进化优化来解决 DCOP。在 AED 中，代理合作构建一组初始随机解决方案，并通过考虑局部收益的乐观近似的新机制逐步改进它们。而且，我们为 AED 提出了一种新的随时更新机制，该机制可以识别分布式候选解决方案中的最佳解决方案，并在找到新的最佳解决方案时通知所有代理。在我们的理论分析中，我们证明了 AED 是任何时候。最后，我们展示了经验结果，表明 AED 在解决方案质量方面优于最先进的 DCOP 算法。