Cooperative coevolution of real predator robots and virtual robots in the pursuit domain
- Publisher:
- ELSEVIER
- Publication Type:
- Journal Article
- Citation:
- APPLIED SOFT COMPUTING, 2020, 89
- Issue Date:
- 2020-04-01
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The pursuit domain, or predator-prey problem is a standard testbed for the
study of coordination techniques. In spite that its problem setup is apparently
simple, it is challenging for the research of the emerged swarm intelligence.
This paper presents a particle swarm optimization (PSO) based cooperative
coevolutionary algorithm for the predator robots, called CCPSO-R, where real
and virtual robots coexist for the first time in an evolutionary algorithm
(EA). Virtual robots sample and explore the vicinity of the corresponding real
robot and act as their action spaces, while the real robots consist of the real
predators swarm who actually pursue the prey robot without fixed behavior rules
under the immediate guidance of the fitness function, which is designed in a
modular manner with very limited domain knowledges. In addition, kinematic
limits and collision avoidance considerations are integrated into the update
rules of robots. Experiments are conducted on a scalable predator robots swarm
with 4 types of preys, the statistical results of which show the reliability,
generality, and scalability of the proposed CCPSO-R. Finally, the codes of this
paper are public availabe at: https://github.com/LijunSun90/pursuitCCPSO_R.
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