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Many-objective multi-tasking optimization using adaptive differential evolutionary and reference-point based nondominated sorting
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AI
DOI：10.1016/j.eswa.2024.123336.png)
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Abstract
En 中文
The multi -objective multi -task evolutionary algorithm is emerging in the field of evolutionary computating. A great deal of multi -objective multi -tasking algorithms have been proposed recently and proved to be superior in solving many problems. However, in the real world, there are numerous high -dimensional objective problems that need to be solved. With the increasing number of objectives, the slow convergence speed, high computational complexity and reduced population diversity will occur in the existing multi -objective multi -tasking algorithms. There is a growing need to study high -dimensional objective algorithms in multitasking environment. To fulfill this research gap, a novel many -objective multi -tasking evolutionary algorithm (MaMTO-ADE) is put forward in this paper. The reference points -based non -dominated sorting method is introduced, which guarantees the diversity of the population in high -dimensional space. And a new offspring generation strategy is proposed to accelerate the population convergence and enables the population to generate high -quality offspring. The performance of MaMTO-ADE is verified on the classical benchmark problems, and the experimental results emphasize the excellent competitiveness of MaMTO-ADE compared to other related algorithms.
Keywords:
Many-objective optimization
Evolutionary computation
Multitask optimization
Knowledge transfer
High dimension
Journal
IF:
7.5
Papers:
2.9W
Citations:
10.2W