Abstract
ABSTRACT This paper presents an in-depth analysis of a Quantum-inspired Multi-objective Optimization Algorithm (QMOA) applied to a unique problem: maximizing trading profits while minimizing energy costs. Previous investigations have explored the profitability of Bitcoin, yet our research delves into its relationship with energy costs. Regarding the trade-offs, the Pareto analysis reveals that trading profit and energy cost do not strongly inversely correlate. The range of outcomes shows a relatively uniform trading profit (from 1.302,85 to 1.310,22$), but a broader variation in energy costs (from 1.141,66 to 5.657,94$). While the trading profit remains stable, there is a wide array of options for minimizing energy cost, which is influenced by various constraints and market conditions. Solutions tend to cluster more in areas of higher energy costs. However, the variability in energy costs offers Bitcoin miners choices, allowing them to tailor strategies, whether that involves prioritizing energy efficiency, profit maximization or striking a balance.
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