  We propose SonOpt, the first (open source) data sonification application for
monitoring the progress of bi-objective population-based optimization
algorithms during search, to facilitate algorithm understanding. SonOpt
provides insights into convergence/stagnation of search, the evolution of the
approximation set shape, location of recurring points in the approximation set,
and population diversity. The benefits of data sonification have been shown for
various non-optimization related monitoring tasks. However, very few attempts
have been made in the context of optimization and their focus has been
exclusively on single-objective problems. In comparison, SonOpt is designed for
bi-objective optimization problems, relies on objective function values of
non-dominated solutions only, and is designed with the user (listener) in mind;
avoiding convolution of multiple sounds and prioritising ease of familiarizing
with the system. This is achieved using two sonification paths relying on the
concepts of wavetable and additive synthesis. This paper motivates and
describes the architecture of SonOpt, and then validates SonOpt for two popular
multi-objective optimization algorithms (NSGA-II and MOEA/D). Experience SonOpt
yourself via https://github.com/tasos-a/SonOpt-1.0 .
