  Communication structure plays a key role in the learning capability of
decentralized systems. Structural self-adaptation, by means of
self-organization, changes the order as well as the input information of the
agents' collective decision-making. This paper studies the role of agents'
repositioning on the same communication structure, i.e. a tree, as the means to
expand the learning capacity in complex combinatorial optimization problems,
for instance, load-balancing power demand to prevent blackouts or efficient
utilization of bike sharing stations. The optimality of structural
self-adaptations is rigorously studied by constructing a novel large-scale
benchmark that consists of 4000 agents with synthetic and real-world data
performing 4 million structural self-adaptations during which almost 320
billion learning messages are exchanged. Based on this benchmark dataset, 124
deterministic structural criteria, applied as learning meta-features, are
systematically evaluated as well as two online structural self-adaptation
strategies designed to expand learning capacity. Experimental evaluation
identifies metrics that capture agents with influential information and their
optimal positioning. Significant gain in learning performance is observed for
the two strategies especially under low-performing initialization. Strikingly,
the strategy that triggers structural self-adaptation in a more exploratory
fashion is the most cost-effective.
