Tensor decomposition-based alternate sub-population evolution for large-scale many-objective optimization

A novel alternate evolution of sub-populations based on tensor decomposition called, TASE is proposed, for solving multi-and many-objective optimization problems with large-scale decision variables in this work. Tensor canonical polyadic (CP) decomposition is introduced for the first time to divide the heterogeneous variables of higher-dimensional decision space into several lower-dimensional sub-components. Furthermore, these sub-populations are optimized alternatively to search for improved solutions in their respective lower-dimensional decision subspace. Finally, a cross-population matching scheme is designed to reconstruct the whole population accurately. The experiments use some lar-gescale multi-and many-objective problems with 2-6 objectives and 1000-5000 variables. The proposed algorithm is compared with other state-of-the-art algorithms, and the exper-imental results indicate that it can solve some problems that other well-known large-scale optimization algorithms cannot, as well as outperforming other algorithms in terms of solution quality and convergence rate. (c) 2021 Elsevier Inc. All rights reserved.

Automated summary

A novel method called TASE is proposed, which is based on tensor decomposition for alternate evolution of sub-populations to solve multi- and many-objective optimization problems with large-scale decision variables. By introducing tensor canonical polyadic (CP) decomposition to divide heterogeneous variables into lower-dimensional sub-components, optimizing these sub-populations alternately in lower-dimensional decision subspaces, and designing a cross-population matching scheme, the algorithm achieves superior solution quality and convergence rate compared to other state-of-the-art algorithms on large-scale problems.