Snake Optimization Algorithm (SOA): A Novel Approach for Blockchain Scalability and Carbon Footprint Minimization
Keywords:
Blockchain, Carbon Footprint, Snake Optimization Algorithm, Proof-of-Work, Green Energy, Metaheuristic, Energy Efficiency, Sustainable ComputingAbstract
While blockchain technology holds promise for decentralized applications, the Proof-of-Work (PoW) consensus protocol consumes significant amounts of energy and thus has a substantial carbon footprint.This paper presents and tests the use of the Snake Optimization Algorithm (SOA), a new swarm-intelligence metaheuristic algorithm, modeled on the behavior of snakes during their mating season, to optimize three key parameters influencing the operation of blockchains: the Tolerance Factor for additional PoW latency (TFT), the Green Node Preference Factor (GPF), and the Workload Throttling Factor (WTF), with the aim of minimizing the carbon footprint.A custom multi-objective fitness function is developed, which minimizes energy consumed per transaction, end-to-end latency, transaction fees, and reliability penalties with an emphasis on sustainability by assigning the most weight to minimize energy consumption (0.45).The results of the simulation under four different renewable-energy penetration scenarios (f_green = 0%, 20%, 50%, 100%) do show that SOA is able to converge within 500 iterations, and that power-per-transaction can be reduced by up to 90% (from 50 W to 5 W) as the availability of green nodes increases.With 50% green penetration, the effective green transaction fraction increases from 0.50 to 0.62 with SOA optimized scheduling.The statistical analysis show that the fitness landscape is always smooth and unimodal when renewable penetration is above 20%, leading to rapid convergence.The proposed framework is the first one that connects metaheuristic optimization to blockchain carbon-footprint minimization, which can be extended to Proof-of-Stake architectures, real-time carbon-intensity-aware scheduling, and multi-objective consensus parameter tuning.
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