Energy Consumption and Search Performance in Surrogate-Assisted Evolutionary Algorithms

Abstract

In recent years, the importance of energy efficiency in computing has increased in the pursuit of a sustainable society. When solving real-world expensive optimization problems, evolutionary algorithms that extensively use iterative computations face challenges of high computational costs and increased energy consumption. This study aims to evaluate how surrogate models can reduce energy consumption to address these issues. Specifically, we applied a particle swarm optimization (PSO) algorithm using a neural network as a surrogate model to a traffic light scheduling problem. This problem requires simulation for solution evaluation, resulting in high evaluation costs. The experimental results demonstrated that a surrogate-assisted PSO can maintain search performance equivalent to a PSO without a surrogate while significantly reducing energy consumption during execution.