  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.
