A multimodal evolutionary algorithm with multi-niche cooperation

Multimodal optimization problems, which involve multiple global optima, are common in real-world applica-tions. So far, plenty of multimodal evolutionary algorithms (MMEAs) have been proposed, where niching techniques are widely utilized to locate different optima by trying to cover each modality with an exclusive niche. However, most existing MMEAs deal with niches independently without considering their similarity and redundancy, which greatly limits the performance of the algorithms. Directing against this issue, this study proposes a multi-niche cooperation based MMEA, where a knowledge transfer strategy (KTS) and a collaborative search mechanism (CSM) are designed. Specifically, given the high similarity shared by different modalities, KTS cooperatively evolves the corresponding niches by transferring knowledge among them, thereby accelerating their convergence. For niches possibly covering the same modality, CSM explicitly measures the search intensity on the modality and adaptively deactivates redundant niches, so that excessive searches on the modality can be avoided. This study incorporates the above two strategies into a classic MMEA named NEA2, and thus leads to a multi-niche cooperation based NEA (MNC-NEA). Experiments conducted on 20 benchmark functions demon-strate that KTS and CSM are efficient and complementary, and they together endow MNC-NEA with a significant competitive advantage over 11 state-of-the-art MMEAs.

Automated summary

In this study, a multi-niche cooperation based MMEA algorithm is proposed to address the issue of independent handling of multiple populations in existing MMEAs. The algorithm includes a knowledge transfer strategy (KTS) and a collaborative search mechanism (CSM). Experiments demonstrate that the combination of KTS and CSM gives MNC-NEA a significant competitive advantage in solving multimodal optimization problems.