Optimizing Carbon Footprint in ICT through Swarm Intelligence with Algorithmic Complexity

TL;DR

The paper tackles ICT-driven CO2 emissions by quantifying emissions from swarm-based ML computations using a deterministic model defined as $CO2 = H(N_p) * sf(N_i) * prod_{k=1}^{n_h} h_k * prod_{l=1}^{n_t} t_l * prod_{m=1}^{n_b} b_m * t_unit * P_h * eta * e_r$, with $H(N_p) = prod_{i=1}^{N_p} i^i$ and $sf(N_i) = prod_{j=1}^{N_i} j!$, and frames data fusion as a convex optimization problem. It evaluates a broad set of swarm methods—from stochastic PSO/FA to hybrid and nature-inspired algorithms—to relate algorithmic complexity to emissions. Key finding: simpler stochastic methods tend to yield lower emissions (roughly 5.25%–6.71%, with hybrids up to 7.87%), highlighting a trade-off between optimization performance and energy footprint. The work provides a scalable benchmarking approach for environmental impact across swarm strategies and points to practical directions for real-world deployment and refinement.

Abstract

Global emissions from fossil fuel combustion and cement production were recorded in 2022, signaling a resurgence to pre-pandemic levels and providing an apodictic indication that emission peaks have not yet been achieved. Significant contributions to this upward trend are made by the Information and Communication Technology (ICT) industry due to its substantial energy consumption. This shows the need for further exploration of swarm intelligence applications to measure and optimize the carbon footprint within ICT. All causative factors are evaluated based on the quality of data collection; variations from each source are quantified; and an objective function related to carbon footprint in ICT energy management is optimized. Emphasis is placed on the asyndetic integration of data sources to construct a convex optimization problem. An apodictic necessity to prevent the erosion of accuracy in carbon footprint assessments is addressed. Complexity percentages ranged from 5.25% for the Bat Algorithm to 7.87% for Fast Bacterial Swarming, indicating significant fluctuations in resource intensity among algorithms. These findings suggest that we were able to quantify the environmental impact of various swarm algorithms.