Journal
SWARM AND EVOLUTIONARY COMPUTATION
Volume 44, Issue -, Pages 113-129Publisher
ELSEVIER
DOI: 10.1016/j.swevo.2018.01.005
Keywords
Swarm computation; Swarm robotics; Bat algorithm; Unknown target location; Behavioral patterns
Funding
- Computer Science National Program of the Spanish Research Agency (Agencia Estatal de Investigacion)
- AEI/FEDER, UE [TIN2017-89275-R]
- project EVOLFORMAS - public body SODERCAN of the Regional Government of Cantabria [JU12]
- European funds FEDER
- project PDE-GIR of the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Actions grant [778035]
- Toho University (Funabashi, Japan)
- University of Cantabria (Santander, Spain)
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Bat algorithm is a powerful nature-inspired swarm intelligence method proposed by Prof. Xin-She Yang in 2010, with remarkable applications in industrial and scientific domains However, to the best of authors' knowledge, this algorithm has never been applied so far in the context of swarm robotics. With the aim to fill this gap, this paper introduces the first practical implementation of the bat algorithm in swarm robotics. Our implementation is performed at two levels: a physical level, where we design and build a real robotic prototype; and a computational level, where we develop a robotic simulation framework. A very important feature of our implementation is its high specialization: all (physical and logical) components are fully optimized to replicate the most relevant features of the real microbats and the bat algorithm as faithfully as possible. Our implementation has been tested by its application to the problem of finding a target location within unknown static indoor 3D environments. Our experimental results show that the behavioral patterns observed in the real and the simulated robotic swarms are very similar. This makes our robotic swarm implementation an ideal tool to explore the potential and limitations of the bat algorithm for real-world practical applications and their computer simulations.
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