Abstract
The Ark protocol introduces a novel Bitcoin scaling solution that enables instant, low cost tx while preserving self custody guarantees. However, the protocol's efficiency critically depends on when Ark operators initiate commitment tx rounds (a decision that involves complex trade-offs between UX, operational costs, and network efficiency). The Ark Litepaper explicitly identifies round timing optimization as an open research problem, noting that "it is a topic for future research to formalise and quantify these incentives."
With this I proposes a multi objective swarm intelligence approach to optimize Ark round timing decisions. I suspect the round timing problem to be a non convex [haven't proved it yet], multi-objective optimization challenge involving conflicting objectives:
- minimizing on chain costs
- minimizing user latency
- maximizing batch efficiency
- ensuring user safety constraints
My approach employs particle swarm optimization(PSO) with Pareto frontier exploration to discover optimal timing strategies that adapt to real-time network conditions. The research will compare swarm-based timing against current fixed-interval approaches, potentially improving Ark protocol efficiency and user experience while contributing novel insights to distributed systems optimization.