Analyzing energy consumption of nature-inspired optimization algorithms

Abstract

Nature-Inspired Optimization (NIO) algorithms have become prevalent to address a variety of optimization problems in real-world applications because of their simplicity, flexibility, and effectiveness. Some application areas of NIO algorithms are telecommunications, image processing, engineering design, vehicle routing, etc. This study presents a critical analysis of energy consumption and their corresponding carbon footprint for four popular NIO algorithms. Microsoft Joulemeter is employed for measuring the energy consumption during the runtime of each algorithm, while the corresponding carbon footprint of each algorithm is calculated based on the UK DEFRA guide. The results of this study evidence that each algorithm demonstrates different energy consumption behaviors to achieve the same goal. In addition, a one-way Analysis of Variance (ANOVA) test is conducted, which shows that the average energy consumption of each algorithm is significantly different from each other. This study will help guide software engineers and practitioners in their selection of an energy-efficient NIO algorithm. As for future work, more NIO algorithms and their variants can be considered for energy consumption analysis to identify the greenest NIO algorithms amongst them all. In addition, future work can also be considered to ascertain possible relationships between NIO algorithms and the energy usage of hardware resources of different CPU architectures.

A Systematic Review of Multi-Objective Evolutionary Algorithms Optimization Frameworks

“…They also emphasize that a large part of energy consumption is determined by the computational complexity of the solution. A comparison between the carbon footprints of four different nature-inspired optimization algorithms (Genetic Algorithm, Particle Swarm Optimization, Differential Evolution, Artificial Bee Colony) implemented in MATLAB is presented in the work presented in [57]. Their findings show Differential Evolution to be the greenest of the four, while Artificial Bee Colony has the highest energy consumption.…”

An Effective Runge-Kutta Optimizer Based on Adaptive Population Size and Search Step Size

“…To promote green optimization algorithms, the carbon footprint of the optimization algorithms [39] is computed. As a result, during the runtime of each algorithm, Microsoft Joulemeter [40] is used to measure the mean power consumption (P) of the MATLAB application. Then the application's energy consumption is calculated for each algorithm using the equation E = Pt where t is the algorithm's runtime.…”