A benchmark generator for dynamic multi-objective optimization problems
Many real-world optimization problems appear to not only have multiple objectives that conflict each other but also change over time. They are dynamic multi-objective optimization problems (DMOPs) and the corresponding field is called dynamic multi-objective optimization (DMO), which has gained growing attention in recent years. However, one main issue in the field of DMO is that there is no standard test suite to determine whether an algorithm is capable of solving them. This paper presents a new benchmark generator for DMOPs that can generate several complicated characteristics, including mixed Pareto-optimal front (convexity-concavity), strong dependencies between variables, and a mixed type of change, which are rarely tested in the literature. Experiments are conducted to compare the performance of five state-of-the-art DMO algorithms on several typical test functions derived from the proposed generator, which gives a better understanding of the strengths and weaknesses of these tested algorithms for DMOPs.
The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.
Citation : Jiang, S. and Yang, S. (2014) A benchmark generator for dynamic multi-objective optimization problems. Proceedings of the 2014 UK Workshops on Computational Intelligence (UKCI), Bradford, UK, September 2014, pp. 1-8, 2014.
ISBN : 9781479955381
Research Institute : Institute of Artificial Intelligence (IAI)
Peer Reviewed : Yes