Evolutionary multitasking cooperative transfer for multiobjective hyperspectral sparse unmixing

Jianzhao Li

doi:10.1016/j.knosys.2023.111306

2024-02-01

PRE

DOI：10.1016/j.knosys.2023.111306

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摘要

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Evolutionary multiobjective optimization is vigorous but not efficient in solving the hyperspectral sparse unmixing problem, while most related algorithms suffer from high computational complexity. This limitation becomes more pronounced in dealing with multiple complex hyperspectral sparse unmixing tasks (HSUTs) concurrently. In fact, different HSUTs are interrelated on spectral libraries and search spaces. However, there is currently no evolutionary algorithm-based approach that explores the cooperativity of multiple HSUTs. Taking advantage of this opportunity, we propose a novel evolutionary multitasking cooperative transfer (EMCT) framework for multiobjective hyperspectral sparse unmixing in this paper. EMCT can handle different HSUTS simultaneously with a uniformly coded population, and discover the correlation between different tasks to promote their convergence. To overcome the issue of unequal dimensions and non-corresponding spectra in the spectral libraries utilized by different tasks, we propose a novel library alignment strategy to alleviate the multitasking negative transfer. In addition, we design novel initialization strategy and genetic operators. to improve the population diversity and the efficiency of population evolution between different HSUTs. We utilize the reconstruction error of every endmember in the spectral library for each task as prior knowledge to evenly distribute the population within the sparsity interval to be explored. In initialization and population evolution, EMCT prefers those decision variables with lower error scores, guiding the population to explore more probable active endmember sets. On the three most commonly used simulated datasets and one real dataset, EMCT achieves state-of-the-art (SOTA) or competitive experimental results. Furthermore, the potential of the EMCT framework is also demonstrated in combination with other multiobjective sparse unmixing algorithms and evolutionary multitasking explicit transfer strategy.

Keyword:

Evolutionary multitasking optimization

Evolutionary multiobjective optimization

Sparse unmixing

Hyperspectral image