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- W4383567307 abstract "Multitasking for multi-objective optimization (MTMO) is one of the most important issues in evolutionary computation. The information exchange mechanism among inter-tasks is the key factor in enhancing the algorithm. Evolutionary multitasking algorithm based on generative strategies (EMT-GS), the current mainstream algorithm, employs a generative adversarial network (GAN) to acquire, propagate and exploit knowledge among tasks, yet GAN suffers from a series of intractable drawbacks, such as training difficulties and mode collapse, etc. To address the issues listed above and achieve better performance, this paper proposes a new algorithm named MTMO-WGAN, which leverages Wasserstein GAN(WGAN) with weight clipping and gradient penalty as the generative strategies to deal with MTMO problems, respectively. Based on the MTMOO benchmark problems, MTMO-WGAN outperforms EMT-GS in the bulk of tasks and has great potential for advancement in the future, which unlocks possibilities for the application of deep generative models in the field of MTMO." @default.
- W4383567307 created "2023-07-08" @default.
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- W4383567307 date "2023-01-01" @default.
- W4383567307 modified "2023-10-17" @default.
- W4383567307 title "A WGAN-Based Generative Strategy in Evolutionary Multitasking for Multi-objective Optimization" @default.
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- W4383567307 doi "https://doi.org/10.1007/978-3-031-36622-2_32" @default.
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