  In parallel with the continuously increasing parameter space dimensionality,
search and optimization algorithms should support distributed parameter
evaluations to reduce cumulative runtime. Intel's neuromorphic optimization
library, Lava-Optimization, was introduced as an abstract optimization system
compatible with neuromorphic systems developed in the broader Lava software
framework. In this work, we introduce Lava Multi-Agent Optimization (LMAO) with
native support for distributed parameter evaluations communicating with a
central Bayesian optimization system. LMAO provides an abstract framework for
deploying distributed optimization and search algorithms within the Lava
software framework. Moreover, LMAO introduces support for random and grid
search along with process connections across multiple levels of mathematical
precision. We evaluate the algorithmic performance of LMAO with a traditional
non-convex optimization problem, a fixed-precision transductive spiking graph
neural network for citation graph classification, and a neuromorphic satellite
scheduling problem. Our results highlight LMAO's efficient scaling to multiple
processes, reducing cumulative runtime and minimizing the likelihood of
converging to local optima.
