An effective knowledge transfer method based on semi-supervised learning for evolutionary optimization

Effective knowledge transfer has been proven to achieve superior performance in evolu-tionary optimization. Evolutionary multitasking optimization (EMT), which can solve sev-eral optimization tasks simultaneously using evolutionary algorithms, continues to be a young research field but is growing rapidly. Due to the parallelism of population-based search, the performance of component tasks can be improved through effective knowledge transfer between different tasks. The main challenge in the EMT field is addressing the neg-ative transfer. Aiming to overcome this challenge, this paper proposes an EMT algorithm that transfers effective knowledge through semi-supervised learning. In addition, a semi -supervised classification method is designed based on the cluster assumption, which is part of the geometric basis of semi-supervised learning. By using both labeled and unlabeled samples generated in the optimization process, the proposed method can identify individ-uals that contain valuable knowledge and select them to transfer the knowledge between tasks. In this way, the performance of the EMT algorithm can be significantly improved. The effectiveness of the proposed method is verified by empirical tests and comparison with two benchmarks. Further, a case study is conducted. The results indicate that the proposed algorithm can achieve highly competitive performance compared with the state-of-the-art EMT algorithms.(c) 2022 Published by Elsevier Inc.

Automated summary

This paper proposes an evolutionary multitasking optimization algorithm that transfers effective knowledge through semi-supervised learning. By utilizing labeled and unlabeled samples generated in the optimization process, the algorithm identifies individuals with valuable knowledge and transfers the knowledge between tasks, leading to significantly improved performance.