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The advancement of technology has enabled the availability of a large amount of data from different sources. In such multi-view datasets, each view provides a particular representation for data objects and produces different partitions. Weighted Multi-view clustering approaches aim to find a suitable consensus clustering taking into consideration both the incompatibility between views and the relevance of features in each view. In this paper, a multi-objective weighted, Multi-view clustering method is presented based on gradient based optimizer. In the developed algorithm, a set of objective functions is considered that optimize the feature weights simultaneously in each view and the cluster centers that provide the optimal partitioning. Each candidate solution in our proposed method is evaluated by the weighted within-cluster compactness of the partitioning obtained from a single view and by the global weighted between-cluster dispersion among the partitioning provided by all views and the negative entropy among all clusters. To validate the clustering performance of developed approach, nine multi-view datasets with different statistical properties were used in this study. In addition, a real-world multi-omics data which contains four multi-omics datasets for cancer subtype discovery with three levels of omics data were considered. Experimental results demonstrate the ability of the new method to generate better clustering results than six popular multi-objective optimizers and ten state-of-the-art multi-view methods according to three measures, which are clustering accuracy, rand index, and normalized mutual information.