Robot swarms offer inherent robustness and the capacity to execute complex, collaborative tasks surpassing the capabilities of single-agent systems. Co-designing these systems is critical, as marginal improvements in individual performance or unit cost compound significantly at scale. However, under traditional frameworks, this scale renders co-design intractable due to exponentially large, non-intuitive design spaces. To address this, we propose SwarmCoDe, a novel Collaborative Co-Evolutionary Algorithm (CCEA) that utilizes dynamic speciation to automatically scale swarm heterogeneity to match task complexity. Inspired by biological signaling mechanisms for inter-species cooperation, the algorithm uses evolved genetic tags and a selectivity gene to facilitate the emergent identification of symbiotically beneficial partners without predefined species boundaries. Additionally, an evolved dominance gene dictates the relative swarm composition, decoupling the physical swarm size from the evolutionary population. We apply SwarmCoDe to simultaneously optimize task planning and hardware morphology under fabrication budgets, successfully evolving specialized swarms of up to 200 agents -- four times the size of the evolutionary population. This framework provides a scalable, computationally viable pathway for the holistic co-design of large-scale, heterogeneous robot swarms.

翻译：机器人集群具有内在的鲁棒性，并能够执行超越单智能体系统能力的复杂协作任务。对此类系统进行协同设计至关重要，因为个体性能的微小提升或单元成本的微量优化都会在规模化时产生显著放大效应。然而，在传统框架下，规模效应导致设计空间呈指数级膨胀且反直觉，使得协同设计难以实现。为此，我们提出SwarmCoDe——一种利用动态特化机制自动扩展集群异构性以匹配任务复杂度的新型协作式协同进化算法（CCEA）。该算法受物种间协作的生物信号传导机制启发，采用进化遗传标签与选择性基因，在无需预设物种边界的前提下促进共生互利伙伴的涌现式识别。此外，通过进化主导基因决定集群的相对组成结构，从而实现物理集群规模与进化种群规模的解耦。我们将SwarmCoDe应用于制造预算约束下任务规划与硬件形态的同步优化，成功进化出包含多达200个智能体的特化集群——规模达到进化种群的四倍。该框架为大规模异构机器人集群的全局协同设计提供了可扩展、低计算成本的实现路径。