Because swarm robots have been applied widely in various fields, the evolution capability of their strategy have become of primary interest; therefore, the evolution method of swarm robots’ strategy has attracted attention in both industry and academia, especially for complex applications owing to their varied task scenes. Large amounts of researches have been conducted to realize strategy evolution in swarm robotics systems. However, there are few studies on the strategy evolution sufficiently examining the simultaneous improvement of evolutionary and strategy performance, which are two key demands of swarm robots. Besides, the strategy that evolved under the global information is difficult to fully adapt to the distributed task scenarios. To address these issues, this study presents a heterogeneous-homogeneous swarm coevolution method known as TORCH to improve the evolution capability of swarm robots. The method uses a swarm coevolution mechanism to accelerate the evolution. For the first time, we employ a behaviour expression tree in TORCH which expands the strategy search space of the evolved strategies. TORCH makes the swarm robots’ strategies evolve under local information conditions; hence, the evolutionary strategies are more adaptable to the distributed task scenarios. Extensive experiments have been conducted to verify the proposed TORCH, including a comparison with three methods based on the homogeneous swarm evolution method and parameter expression. The results demonstrate the superiority of the TORCH in terms of evolutionary efficiency improvement and strategy performance enhancement.

由于群体机器人在各个领域得到广泛应用，其策略的进化能力成为首要关注的问题；因此，群体机器人策略的演化方法在工业界和学术界都引起了广泛关注，尤其是在任务场景多样的复杂应用中。已经进行了大量研究以实现群体机器人系统中的策略演化。然而，很少有关于策略进化的研究充分检验进化和策略性能的同时改进，这是群体机器人的两个关键需求。此外，在全局信息下进化的策略很难完全适应分布式任务场景。为了解决这些问题，本研究提出了一个他牛逼erogeneous小时ø mogeneous SWA ř米Ç oevolution满足ħod 被称为 TORCH，以提高群机器人的进化能力。该方法使用群体协同进化机制来加速进化。我们第一次在 TORCH 中使用了行为表达树，扩展了进化策略的策略搜索空间。TORCH使群机器人的策略在局部信息条件下进化；因此，进化策略更适合分布式任务场景。已经进行了大量实验来验证所提出的 TORCH，包括与基于同构群进化方法和参数表达式的三种方法的比较。结果证明了TORCH在进化效率提升和策略性能增强方面的优越性。