1 research outputs found
Analysis of Evolved Response Thresholds for Decentralized Dynamic Task Allocation
We investigate the application of a multi-objective genetic algorithm to the
problem of task allocation in a self-organizing, decentralized, threshold-based
swarm. Each agent in our system is capable of performing four tasks with a
response threshold for each, and we seek to assign response threshold values to
all of the agents a swarm such that the collective behavior of the swarm is
optimized. Random assignment of threshold values according to a uniform
distribution is known to be effective; however, this method does not consider
features of particular problem instances. Dynamic response thresholds have some
flexibility to address problem specific features through real-time adaptivity,
often improving swarm performance.
In this work, we use a multi-objective genetic algorithm to evolve response
thresholds for a simulated swarm engaged in a dynamic task allocation problem:
two-dimensional collective tracking. We show that evolved thresholds not only
outperform uniformly distributed thresholds and dynamic thresholds but achieve
nearly optimal performance on a variety of tracking problem instances (target
paths). More importantly, we demonstrate that thresholds evolved for one of
several problem instances generalize to all other problem instances eliminating
the need to evolve new thresholds for each problem to be solved. We analyze the
properties that allow these paths to serve as universal training instances and
show that they are quite natural.Comment: 22 pages, 12 figure