Surrogate-Assisted Adaptive Knowledge Transfer for Expensive Multitasking Optimization | IEEE Conference Publication | IEEE Xplore

Surrogate-Assisted Adaptive Knowledge Transfer for Expensive Multitasking Optimization


Abstract:

Leveraging on fruitful intertask knowledge transfer, multitasking evolutionary algorithms (MTEAs) exhibit superior efficiency in handling multiple optimization tasks simu...Show More

Abstract:

Leveraging on fruitful intertask knowledge transfer, multitasking evolutionary algorithms (MTEAs) exhibit superior efficiency in handling multiple optimization tasks simultaneously. In practice, it is common that the fitness evaluation of tasks is computationally expensive, leading to a very limited number of fitness evaluations for MTEAs. With this in mind, we propose a radial basis functions-assisted MTEA (RAMTEA) in this paper to better solve expensive multitasking optimization problems. In the proposed method, radial basis functions are constructed to approximate each task's real function to guide the selection of new samples. Furthermore, an adaptive sampling strategy considering intertask similarities is applied to facilitate the convergence of multiple tasks and curb negative transfer. The efficacy of our proposal is demonstrated by experimental studies including ablation experiments and comparison with advanced MTEAs on widely used benchmark problems.
Date of Conference: 30 June 2024 - 05 July 2024
Date Added to IEEE Xplore: 08 August 2024
ISBN Information:
Conference Location: Yokohama, Japan

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