  How to simultaneously locate multiple global peaks and achieve certain
accuracy on the found peaks are two key challenges in solving multimodal
optimization problems (MMOPs). In this paper, a landscape-aware differential
evolution (LADE) algorithm is proposed for MMOPs, which utilizes landscape
knowledge to maintain sufficient diversity and provide efficient search
guidance. In detail, the landscape knowledge is efficiently utilized in the
following three aspects. First, a landscape-aware peak exploration helps each
individual evolve adaptively to locate a peak and simulates the regions of the
found peaks according to search history to avoid an individual locating a found
peak. Second, a landscape-aware peak distinction distinguishes whether an
individual locates a new global peak, a new local peak, or a found peak.
Accuracy refinement can thus only be conducted on the global peaks to enhance
the search efficiency. Third, a landscape-aware reinitialization specifies the
initial position of an individual adaptively according to the distribution of
the found peaks, which helps explore more peaks. The experiments are conducted
on 20 widely-used benchmark MMOPs. Experimental results show that LADE obtains
generally better or competitive performance compared with seven well-performed
algorithms proposed recently and four winner algorithms in the IEEE CEC
competitions for multimodal optimization.
