Empirical Study on the Benefits of Multiobjectivization for Solving Single-Objective Problems
This addresses optimization challenges for researchers and practitioners in fields like engineering or machine learning, but it appears incremental as it applies an existing multi-objective method to a known bottleneck.
The study tackled the problem of multimodality in continuous single-objective optimization by multiobjectivizing it with an additional objective, and empirically showed that the multi-objective optimizer MOGSA overcomes local traps, outperforming the Nelder-Mead optimizer on COCO benchmark functions.
When dealing with continuous single-objective problems, multimodality poses one of the biggest difficulties for global optimization. Local optima are often preventing algorithms from making progress and thus pose a severe threat. In this paper we analyze how single-objective optimization can benefit from multiobjectivization by considering an additional objective. With the use of a sophisticated visualization technique based on the multi-objective gradients, the properties of the arising multi-objective landscapes are illustrated and examined. We will empirically show that the multi-objective optimizer MOGSA is able to exploit these properties to overcome local traps. The performance of MOGSA is assessed on a testbed of several functions provided by the COCO platform. The results are compared to the local optimizer Nelder-Mead.