Unidirectional Transfer Differential Evolution Algorithm Based on Joint Distribution Adaptation

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Unidirectional Transfer Differential Evolution Algorithm Based on Joint Distribution Adaptation

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[1]LI Xi,LI Shuai,FENG Yanhong,et al.Unidirectional Transfer Differential Evolution Algorithm Based on Joint Distribution Adaptation[J].Journal of Zhengzhou University (Engineering Science),2023,44(05):24-31.[doi:10.13705/j.issn.1671-6833.2023.05.005]

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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 44卷 Number of periods: 2023 05 Page number: 24-31 Column: Public date: 2023-08-20

Title:: Unidirectional Transfer Differential Evolution Algorithm Based on Joint Distribution Adaptation

Author(s):: LI Xi ; LI Shuai; FENG Yanhong; LI Mingliang

Keywords:: ptimization algorithms; transfer learning; joint distribution adaptation; unidirectional transfer; differential evolutionary

CLC:: O234；TP301

DOI:: 10.13705/j.issn.1671-6833.2023.05.005

Abstract:: In traditional differential evolution algorithm solves optimization problems generally starting from zero-knowledge and searching independently without using the information of similar problems that have been solved. In this paper, the transfer learning technique was introduced into DE. Firstly, the extracted key information from the optimized population of the source problem and the current population of the target problem was mapped to the high-dimensional Hilbert space by the joint distribution adaptation method. Then a new population was constructed from the mapped matrix to replace the population of the target problem. Lastly, the subsequent evolution was completed. Two transfer modes were implemented: transferring the effective information of the source problem to guide the search direction of the algorithm during the initialization; utilizing the transferred effective information after a certain number of iterations to accelerate the population convergence. The proposed algorithm was tested with nine sets of multi-task test functions, and compared with the DE without transfer and transferred DE without adaptation technique. The results showed that, the proposed algorithm outperforms the traditional DE on seven sets functions in term of solution quality. Moreover, the proposed algorithm converged faster than DE. Therefore, the transfer learning-based differential evolution algorithm was effective in improving the solution accuracy and convergence speed of the objective optimization problem.

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Last Update: 2023-09-04