  Recent advances in the visualization of continuous multimodal multi-objective
optimization (MMMOO) landscapes brought a new perspective to their search
dynamics. Locally efficient (LE) sets, often considered as traps for local
search, are rarely isolated in the decision space. Rather, intersections by
superposing attraction basins lead to further solution sets that at least
partially contain better solutions. The Multi-Objective Gradient Sliding
Algorithm (MOGSA) is an algorithmic concept developed to exploit these
superpositions. While it has promising performance on many MMMOO problems with
linear LE sets, closer analysis of MOGSA revealed that it does not sufficiently
generalize to a wider set of test problems. Based on a detailed analysis of
shortcomings of MOGSA, we propose a new algorithm, the Multi-Objective
Landscape Explorer (MOLE). It is able to efficiently model and exploit LE sets
in MMMOO problems. An implementation of MOLE is presented for the bi-objective
case, and the practicality of the approach is shown in a benchmarking
experiment on the Bi-Objective BBOB testbed.
