Empirical Study on the Benefits of Multiobjectivization for Solving Single-Objective Problems

• URL: http://arxiv.org/abs/2006.14423v1
• Date: Thu, 25 Jun 2020 14:04:37 GMT
• Title: Empirical Study on the Benefits of Multiobjectivization for Solving Single-Objective Problems
• Authors: Vera Steinhoff and Pascal Kerschke and Christian Grimme
• Abstract summary: Local optima are often preventing algorithms from making progress and thus pose a severe threat. 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 COCO MOGSA is able to exploit these properties to overcome local traps.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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