Energy and Quality of Surrogate-Assisted Search Algorithms: a First Analysis

TL;DR

This paper investigates the energy consumption and accuracy of surrogate-assisted particle swarm optimization, providing initial insights into their efficiency and potential for holistic evaluation of optimization algorithms.

Contribution

It offers the first analysis of energy profiles and surrogate accuracy in particle swarm optimization with neural network surrogates.

Findings

Surrogates impact energy consumption in PSO.

Neural network surrogates can effectively guide search.

Energy and accuracy metrics provide a holistic evaluation approach.

Abstract

Solving complex real problems often demands advanced algorithms, and then continuous improvements in the internal operations of a search technique are needed. Hybrid algorithms, parallel techniques, theoretical advances, and much more are needed to transform a general search algorithm into an efficient, useful one in practice. In this paper, we study how surrogates are helping metaheuristics from an important and understudied point of view: their energy profile. Even if surrogates are a great idea for substituting a time-demanding complex fitness function, the energy profile, general efficiency, and accuracy of the resulting surrogate-assisted metaheuristic still need considerable research. In this work, we make a first step in analyzing particle swarm optimization in different versions (including pre-trained and retrained neural networks as surrogates) for its energy profile (for both processor and memory), plus a further study on the surrogate accuracy to properly drive the search towards an acceptable solution. Our conclusions shed new light on this topic and could be understood as the first step towards a methodology for assessing surrogate-assisted algorithms not only accounting for time or numerical efficiency but also for energy and surrogate accuracy for a better, more holistic characterization of optimization and learning techniques.