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Enhancing evolutionary multitasking optimization by leveraging inter-task knowledge transfers and improved evolutionary operators
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AI
DOI：10.1016/j.knosys.2022.110027.png)
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Abstract
En 中文
It is inefficient and time-consuming to begin the search from scratch for each optimization task. Evolutionary multitasking optimization (EMTO) handles multiple tasks simultaneously, aiming at im-proving the solving quality of every task via the evolutionary algorithm (EA) and inter-task knowledge transfer. Thus, suitable evolutionary operators and inter-task effective knowledge transfer are two key factors for the success of EMTO. As one of the representative EMTO algorithms, the multifactorial evolutionary algorithm (MFEA) has attracted a lot of attention. However, MFEA has suffered from the issue of premature convergence and negative knowledge transfer among tasks with a low correlation. To handle these issues, this article enhances MFEA with two proposed strategies, namely a carefully -designed opposition-based learning (OBL) strategy and a carefully-designed differential evolution (DE) strategy, named MFDE-OBL for short. Both the proposed OBL and the proposed DE contain an inter -task strategy and an intra-task strategy. To improve the effectiveness of knowledge transfer, the inter-task OBL strategy learns a linear subspace mapping among task/tasks' subpopulations to transfer different search scales among tasks, while the inter-task DE strategy uses genetic information from another task to improve the population diversity with different scales and directions. Besides, the intra-task generalized-opposite-point-based OBL is used to enhance the global search ability, while the intra-task DE strategy consists of two complementary DE strategies to maintain a good balance between exploitation and exploration. Finally, the proposed algorithm is tested on both single-objective and multi-objective multi-tasking test suites. Experimental results have shown the efficiency and effectiveness of the proposed algorithm compared with both classical and state-of-the-art algorithms.(c) 2022 Published by Elsevier B.V.
Keywords:
Evolutionary multitasking optimization
Multifactorial evolutionary algorithm
Inter -task knowledge transfer
Opposition -based learning
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7.6
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1.2W
Citations:
4.5W
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