Physical stigmergy for decentralized constrained optimization: an intelligent lighting example

ABSTRACT

Conventional distributed solutions for optimization problems with inseparable constraints require significant coordination between agents. Here, a novel numerical approach is described that achieves coordination via stigmergy -- agents communicate indirectly through modifications of the environment. This approach is designed for optimal resource allocation problems where every solution exists on a constraint boundary; these boundaries provide the environmental cues that guide the collective motion of the distributed actuators without formal communication between them. Theoretical and experimental results validate this approach for an intelligent lighting example; despite the lack of direct coordination, Pareto-optimal allocations are stabilized. The general approach of using physical stigmergic memory may be useful in many other cyber-physical systems.

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