Many-objective many-task optimization using reference-points-based nondominated sorting approach

Abstract

Multi-task optimization utilizes knowledge transfer to optimize multiple tasks simultaneously. When the number of tasks is increased to many-task optimization, the computational burden of the algorithm increases and the positive knowledge transfer rate decreases, which leads to the algorithm performance degradation. In the face of high-dimensional objective space, the existing many-task optimization algorithms have some problems, such as decreasing population diversity and slowing down the search speed of the optimal solution. For many-objective and many-task optimization problems, we propose a many-objective many-task optimization using reference-points-based nondominated sorting approach (MOMaTO-RP). MOMaTO-RP can use multiple tasks with high similarity for knowledge transfer, and the population diversity can also be maintained in the high-dimensional objective space, which greatly improves the population convergence speed. The proposed algorithm is compared with other related algorithms on the classical benchmark set. The results show that the algorithm has a faster convergence speed and better distribution performance.