A Feature Rich Distance-Based Many-Objective Visualisable Test Problem Generator
Fieldsend, JE; Chugh, T; Allmendinger, R; et al.Miettinen, K
Date: 13 July 2019
Conference paper
Publisher
Association for Computing Machinery (ACM)
Publisher DOI
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Abstract
In optimiser analysis and design it is informative to visualise how a
search point/population moves through the design space over time.
Visualisable distance-based many-objective optimisation problems
have been developed whose design space is in two-dimensions with
arbitrarily many objective dimensions. Previous work has shown
how ...
In optimiser analysis and design it is informative to visualise how a
search point/population moves through the design space over time.
Visualisable distance-based many-objective optimisation problems
have been developed whose design space is in two-dimensions with
arbitrarily many objective dimensions. Previous work has shown
how disconnected Pareto sets may be formed, how problems can
be projected to and from arbitrarily many design dimensions, and
how dominance resistant regions of design space may be defined.
Most recently, a test suite has been proposed using distances to lines
rather than points. However, active use of visualisable problems has
been limited. This may be because the type of problem characteristics
available has been relatively limited compared to many practical
problems (and non-visualisable problem suites). Here we introduce
the mechanisms required to embed several widely seen problem
characteristics in the existing problem framework. These include
variable density of solutions in objective space, landscape discontinuities,
varying objective ranges, neutrality, and non-identical
disconnected Pareto set regions. Furthermore, we provide an automatic
problem generator (as opposed to hand-tuned problem definitions).
The flexibility of the problem generator is demonstrated
by analysing the performance of popular optimisers on a range of
sampled instances.
Computer Science
Faculty of Environment, Science and Economy
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