SonOpt: understanding the behaviour of bi-objective population-based optimisation algorithms through sound

Tasos Asonitis, Richard Allmendinger, Matt Benatan, Ricardo Climent

Research output: Contribution to journalArticlepeer-review

Abstract

We present an extension of SonOpt, the first ever openly available tool for the sonification of bi-objective population-based optimisation algorithms. SonOpt has already introduced benefits on the understanding of algorithmic behaviour by proposing the use of sound as a medium for the process monitoring of bi-objective optimisation algorithms. The first edition of SonOpt utilised two different sonification paths to provide information on convergence, population diversity, recurrence of objective values across consecutive generations and the shape of the approximation set. The present extension provides further insight through the introduction of a third sonification path, which involves hypervolume contributions to facilitate the understanding of the relative importance of non-dominated solutions. Using a different sound generation approach than the existing ones, this newly proposed sonification path utilizes pitch deviations to highlight the distribution of hypervolume contributions across the approximation set. To demonstrate the benefits of SonOpt we compare the sonic results obtained from two popular population-based multi-objective optimisation algorithms, Non-Dominated Sorting Genetic Algorithm (NSGA-II) and Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D), and use a Multi-objective Random Search (MRS) approach as a baseline. The three algorithms are applied to numerous test problems and showcase how sonification can reveal various aspects of the optimisation process that may not be obvious from visualisation alone. SonOpt is available for download at https://github.com/tasos-a/SonOpt-2.0.
Original languageEnglish
JournalGenetic Programming and Evolvable Machines
Volume24
Issue number3
DOIs
Publication statusPublished - 13 Mar 2023

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