Multiobjectivization of Local Search: Single-Objective Optimization Benefits From Multi-Objective Gradient Descent
Abstract
Multimodality is one of the biggest difficulties for optimization as local optima are often preventing algorithms from making progress. This does not only challenge local strategies that can get stuck. It also hinders meta-heuristics like evolutionary algorithms in convergence to the global optimum. In this paper we present a new concept of gradient descent, which is able to escape local traps. It relies on multiobjectivization of the original problem and applies the recently proposed and here slightly modified multi-objective local search mechanism MOGSA. We use a sophisticated visualization technique for multi-objective problems to prove the working principle of our idea. As such, this work highlights the transfer of new insights from the multi-objective to the single-objective domain and provides first visual evidence that multiobjectivization can link single-objective local optima in multimodal landscapes.
Cite
@article{arxiv.2010.01004,
title = {Multiobjectivization of Local Search: Single-Objective Optimization Benefits From Multi-Objective Gradient Descent},
author = {Vera Steinhoff and Pascal Kerschke and Pelin Aspar and Heike Trautmann and Christian Grimme},
journal= {arXiv preprint arXiv:2010.01004},
year = {2020}
}
Comments
This version has been accepted for publication at the IEEE Symposium Series on Computational Intelligence (IEEE SSCI) 2020