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.

翻译：如何同时定位多个全局峰值并对已发现峰值达到一定精度，是解决多模态优化问题（MMOPs）面临的两个关键挑战。本文提出一种面向MMOPs的景观感知差分进化（LADE）算法，该算法利用景观知识维持充分的种群多样性并提供高效的搜索引导。具体而言，景观知识在以下三个方面得到有效利用：首先，景观感知的峰值探索通过自适应演化帮助个体定位峰值，并根据搜索历史模拟已发现峰值的区域以避免个体重复定位；其次，景观感知的峰值判别机制能够区分个体所处位置属于新全局峰值、新局部峰值还是已发现峰值，从而可仅对全局峰值进行精度细化以提升搜索效率；第三，景观感知的重新初始化机制根据已发现峰值的分布自适应确定个体的初始位置，有助于探索更多峰值。实验在20个广泛使用的基准MMOPs上进行，结果表明：与近期提出的七种高性能算法以及IEEE CEC多模态优化竞赛中的四种优胜算法相比，LADE在整体上取得了更优或具有竞争力的性能。