Most of the existing multi-view unsupervised feature selection methods have the following problem： the similarity matrix of samples， the weight matrix of different views， and the feature weight matrix are usually predefined， and cannot effectively describe the real intrinsic structure of data and reflect the importance of different views and features， which results in the failure of selection of useful features. In order to address the above issue， firstly， adaptive learning of view weight and feature weight was performed on the basis of multi-view fuzzy C-means clustering， thereby achieving feature selection and guaranteeing the clustering performance simultaneously. Then， under the constraint of Laplacian rank， the similarity matrix of samples was learned adaptively， and an Adaptive Learning-based Multi-view Unsupervised Feature Selection （ALMUFS） method was constructed. Finally， an alternate iterative optimization algorithm was designed to solve the objective function， and the proposed method was compared with six unsupervised feature selection baseline methods on eight real datasets. Experimental results show that ALMUFS is superior to other methods in terms of clustering accuracy and F-measure. In specific， ALMUFS method improves the clustering accuracy and F-measure by 8.99 and 11.87 percentage points compared to Adaptive Collaborative Similarity Learning （ACSL） averagely and respectively and by 11.09 and 13.21 percentage points compared to Adaptive Similarity and View Weight （ASVM） averagely and respectively， which demonstrates the feasibility and effectiveness of the proposed method.

Aiming at the shortcomings of Equilibrium Optimizer （EO） such as low optimization accuracy， slow convergence and being easy to fall into local optimum， a new EO in consideration with distance factor and Elite Evolutionary Strategy （EES） named E-SFDBEO was proposed. Firstly， the distance factor was introduced to select the candidate solutions of the equilibrium pool， and the adaptive weight was used to balance the fitness value and distance， thereby adjusting the exploration and development capabilities of the algorithm in different iterations. Secondly， the EES was introduced to improve the convergence speed and accuracy of the algorithm by both elite natural evolution and elite random mutation. Finally， the adaptive t-distribution mutation strategy was used to perturb some individuals， and the individuals were retained with greedy strategy， so that the algorithm was able to jump out of the local optimum effectively. In the simulation experiment， the proposed algorithm was compared with 4 basic algorithms and 2 improved algorithms based on 10 benchmark test functions and Wilcoxon rank sum test was performed to the algorithms. The results show that the proposed algorithm has better convergence and higher solution accuracy.