Trustless parallel local search for effective distributed algorithm discovery
This addresses the scaling limitation for distributed algorithm discovery in metaheuristic search, moving beyond trusted clusters to broader, untrusted environments.
The paper tackles the problem of scaling parallel local search to untrusted, anonymous computational nodes by proposing a novel blockchain protocol that enables publicly verifiable performance evaluation and economic incentives, resulting in a system that coordinates nodes to explore different search spaces competitively.
Metaheuristic search strategies have proven their effectiveness against man-made solutions in various contexts. They are generally effective in local search area exploitation, and their overall performance is largely impacted by the balance between exploration and exploitation. Recent developments in parallel local search explore methods to take advantage of the efficient local exploitation of searches and reach impressive results. This however restricts the scaling potential to nodes within a private, trusted computer cluster. In this research we propose a novel blockchain protocol that allows parallel local search to scale to untrusted and anonymous computational nodes. The protocol introduces publicly verifiable performance evaluation of the local optima reported by each node, creating a competitive environment between the local searches. That is strengthened with economical stimuli for producing good solutions, that provide coordination between the nodes, as every node tries to explore different sections of the search space to beat their competition.