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Advisor(s)
Abstract(s)
The Darwinian Particle Swarm Optimization (DPSO) is an evolutionary algorithm that extends the Particle
Swarm Optimization using natural selection to enhance the ability to escape from sub-optimal solutions.
An extension of the DPSO to multi-robot applications has been recently proposed and denoted as Robotic
Darwinian PSO (RDPSO), benefiting from the dynamical partitioning of the whole population of robots,
hence decreasing the amount of required information exchange among robots. This paper further extends
the previously proposed algorithm adapting the behavior of robots based on a set of context-based
evaluation metrics. Those metrics are then used as inputs of a fuzzy system so as to systematically
adjust the RDPSO parameters (i.e., outputs of the fuzzy system), thus improving its convergence rate,
susceptibility to obstacles and communication constraints. The adapted RDPSO is evaluated in groups
of physical robots, being further explored using larger populations of simulated mobile robots within a
larger scenario.
Description
Keywords
Foraging Swarm robotics Parameter adjustment Fuzzy logic Context-based information Adaptive behavior
