AMORA-Adaptive Multi-Objective Routing Algorithm in Payment Channel Networks

Blockchain offers an efficient, reliable, and secure environment for performing transactions. Scalability, high transaction fees, routing services, and low throughput are some of the primary challenges of blockchain-based cryptocurrencies. Off-chain transactions are used to tackle these challenges. Payment Channel Networks (PCNs) are developed for implementing off-chain transactions, which require routing algorithms for making successful payments between users. The existing literature proposes many routing algorithms for these transactions in PCNs. However, existing routing algorithms in PCNs considered only a single objective while performing routing. Our proposed Adaptive Multi-Objective Routing algorithm (AMORA) considers multiple objectives that enhance the cost-effectiveness, throughput, and network resiliency in PCNs. Additionally, it reduces the hop count for transactions to retain long-term sustainability. AMORA is based on a local search-based memetic algorithm (MA). MA improves routing through multi-objective optimization. Rigorous simulation evaluation demonstrates that AMORA optimizes transaction time by 31.10%, 47.2%, 46.75%, 33.47%, 48.38%, and 74.3% and increases throughput by 55.34%, 40%, 29.73%, 57.69%, 50.78%, 59.81% compared to the Fence, Dijkstra, MILPA-PCN, SpeedyMurmurs, Spider, and Flash algorithms, respectively. Furthermore, we carry out a theoretical analysis of AMORA’s convergence and compute computational complexity.