Algorithm selection refers to selecting an algorithm that satisfies the requirements for a given problem from feasible algorithms, and algorithm selection based on meta-learning is a widely used method, in which the key components are meta-features and meta-learners. However, existing research is difficult to make full use of the complementarity of meta-features and the diversity of meta-learners, which are not conducive to further improving the method performance. To solve the above problems, a selective ensemble algorithm selection method based on multi-objective hybrid ant lion optimizer (SAMO) is proposed. It designs an algorithm selection model, which sets the accuracy and diversity of the ensemble meta-learners as the optimization objectives, introduces meta-feature selection and selective ensemble, and chooses meta-features and heterogeneous meta-learners simultaneously to construct ensemble meta-learners; it proposes a multi-objective hybrid ant lion optimizer to optimize the model, which uses discrete code to select meta-feature subsets and constructs ensemble meta-learners by continuous code, and applies the enhanced walk strategy and the preference elite selection mechanism to improve the optimization performance. We utilize 260 datasets, 150 meta-features, and 9 candidate algorithms to construct classification algorithm selection problems and conduct test experiments, and the parameter sensitivity of the method is analyzed, the multi-objective hybrid ant lion optimizer is compared with four evolutionary algorithms, 8 comparative methods are compared with the proposed method, and the results verify the effectiveness and superiority of the method.