Energy Efficiency in Search Algorithms: A Comparative Study

In a world where computers use most of its energy, making software more efficient is as important as advancing hardware. How algorithm design works with energy efficiency is explored in this study by comparing two simple search algorithms: naive (linear) search and binary search. Through Python simulations on ten trials for 128-element datasets, this work quantifies the computational steps required to discover a target value based on the premise that they might be employed as an energy measure. The results indicate that binary search consistently outperforms naive search and requires about thirteen times fewer steps on average. This staggering reduction demonstrates the energy-efficient benefits from algorithmic optimization. The conclusion emphasizes that even simple algorithmic choices have great effects on energy efficiency, especially when scaled up to millions of operations in real systems. Aside from its computational importance, the research is also an educational model that allows students to visualize the actual-world effect of algorithm design on sustainability. The study fosters the incorporation of energy-awareness in algorithm design in order to enhance the grand agenda for green and sustainable computing.