用进化算法优化光驱动微机器人的游泳速度，实现八倍提速。
Survival of the fastest -- algorithm-guided evolution of light-powered underwater microrobots
- 用粒子群与遗传算法，以实测速度为优化目标
- 多代进化后速度提升八倍，发现自振泳动新模式
- 适合对微机器人设计与进化计算感兴趣的读者
软体机器人在不同参数下可表现出多种运动模式，数值建模困难。在低雷诺数的小尺度系统中，气液动力学过程复杂，性能优化尤为挑战。本文通过实验测量毫米级水下光驱动微机器人的游泳速度，将其作为适应度函数，应用粒子群优化和遗传算法进行优化。由于不同表型的软体机器人可快速制造，可利用大量竞争机器人在多代中持续提升游泳速度。有趣的是，如同自然进化，意外的基因组合带来了惊人结果：速度最高提升八倍，并发现一种自振荡水下运动模式。
原文摘要 · Abstract (English)
Depending on multiple parameters, soft robots can exhibit different modes of locomotion that are difficult to model numerically. As a result, improving their performance is complex, especially in small-scale systems characterized by low Reynolds numbers, when multiple aero- and hydrodynamical processes influence their movement. In this work, we optimize light-powered millimetre-scale underwater swimmer locomotion by applying experimental results - measured swimming speed - as the fitness function in two evolutionary algorithms: particle swarm optimization and genetic algorithm. As these soft, light-powered robots with different characteristics (phenotypes) can be fabricated quickly, they provide a great platform for optimisation experiments, using many competing robots to improve swimming speed over consecutive generations. Interestingly, just like in natural evolution, unexpected gene combinations led to surprisingly good results, including eight-fold increase in speed or the discovery of a self-oscillating underwater locomotion mode.
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