Evolutionary Multitasking for Multiobjective Optimization With Subspace Alignment and Adaptive Differential Evolution

doi:10.1109/TCYB.2020.2980888

2022-04-01

PRE

DOI：10.1109/TCYB.2020.2980888

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摘要

En 中文

In contrast to the traditional single-tasking evolutionary algorithms, evolutionary multitasking (EMT) travels in the search space of multiple optimization tasks simultaneously. Through sharing knowledge across the tasks, EMT is able to enhance solving the optimization tasks. However, if knowledge transfer is not properly carried out, the performance of EMT might become unsatisfactory. To address this issue and improve the quality of knowledge transfer among the tasks, a novel multiobjective EMT algorithm based on subspace alignment and self-adaptive differential evolution (DE), namely, MOMFEA-SADE, is proposed in this article. Particularly, a mapping matrix obtained by subspace learning is used to transform the search space of the population and reduce the probability of negative knowledge transfer between tasks. In addition, DE characterized by a self-adaptive trial vector generation strategy is introduced to generate promising solutions based on previous experiences. The experimental results on multiobjective multi/many-tasking optimization test suites show that MOMFEA-SADE is superior or comparable to other state-of-the-art EMT algorithms. MOMFEA-SADE also won the Competition on Evolutionary Multitask Optimization (the multitask multiobjective optimization track) within IEEE 2019 Congress on Evolutionary Computation.

Keyword:

Task analysis

Optimization

Knowledge transfer

Multitasking

Sociology

Statistics

Evolutionary computation

Differential evolution (DE)

evolutionary multitasking (EMT)

many-tasking optimization

multiobjective optimization

subspace learning

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期刊

IEEE Transactions on Cybernetics

IF:

10.5

论文数:

1.1W

被引数:

5.0W

机构

shenzhen university

学者数:

4.5W

论文数: 3.4W

被引数: 72

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