Multi-objective evolutionary multi-tasking algorithm using cross-dimensional and prediction-based knowledge transfer

Transfer learning is an important research topic in machine learning and recently has been introduced into evolutionary computation to form evolutionary multi-task optimization (EMTO). EMTO focuses on tackling multiple optimization tasks simultaneously based on knowledge transfer and reuse. Many EMTO algorithms have been proposed mainly for single-objective optimization problems and achieved success in various fields, yet multi objective EMTO remains a big challenge. The existing multi-objective EMTO algorithms tend to suffer from issues like slow convergence and degraded performance on less correlated tasks. To alleviate these issues, this paper proposes a new multi-objective EMTO algorithm by introducing cross-dimensional variable search and prediction-based individual search for efficient knowledge transfer. The cross-dimensional variable search optimizes a decision variable using information collected from other variables. The prediction based individual search performs individual mapping where the offspring solutions and the corresponding parent solutions are symmetrized about the predicted population center to maintain the population diversity. The proposed algorithm is tested on benchmark problems and the experimental results demonstrate the effectiveness and efficiency of the algorithm.(c) 2021 Elsevier Inc. All rights reserved.

Automated summary

This paper proposes a new multi-objective evolutionary multi-task optimization (EMTO) algorithm by introducing cross-dimensional variable search and prediction-based individual search for efficient knowledge transfer. The algorithm is tested on benchmark problems and the experimental results demonstrate its effectiveness and efficiency.