  The present survey provides the state-of-the-art of research, copiously
devoted to Evolutionary Approach (EAs) for clustering exemplified with a
diversity of evolutionary computations. The Survey provides a nomenclature that
highlights some aspects that are very important in the context of evolutionary
data clustering. The paper missions the clustering trade-offs branched out with
wide-ranging Multi Objective Evolutionary Approaches (MOEAs) methods. Finally,
this study addresses the potential challenges of MOEA design and data
clustering, along with conclusions and recommendations for novice and
researchers by positioning most promising paths of future research. MOEAs have
substantial success across a variety of MOP applications, from pedagogical
multifunction optimization to real-world engineering design. The survey paper
noticeably organizes the developments witnessed in the past three decades for
EAs based metaheuristics to solve multiobjective optimization problems (MOP)
and to derive significant progression in ruling high quality elucidations in a
single run. Data clustering is an exigent task, whose intricacy is caused by a
lack of unique and precise definition of a cluster. The discrete optimization
problem uses the cluster space to derive a solution for Multiobjective data
clustering. Discovery of a majority or all of the clusters (of illogical
shapes) present in the data is a long-standing goal of unsupervised predictive
learning problems or exploratory pattern analysis.
