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A Multiobjective Multitask Optimization Algorithm Using Transfer Rank
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DOI：10.1109/TEVC.2022.3147568.png)
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Abstract
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Multiobjective multitask optimization (MMO) attempts to solve several problems simultaneously. This is commonly done by identifying useful knowledge to transfer between tasks, thereby producing optimal solutions more quickly. In this study, an MMO algorithm using transfer rank and a KNN model is proposed to achieve this goal. The definition of transfer rank is first introduced for quantifying the priority of transfer solutions, to improve the probability of a positive result. The solution with the higher rank was assumed to be the most suitable for transfer, as solutions were sorted in descending order based on transfer rank. Priority was given to previous and positive-transfer solutions and those with the same transfer rank were distinguished using a KNN model classifier. The effectiveness of the proposed algorithm was verified by studying benchmark MMO problems. The experimental results showed the proposed algorithm was more effective than other conventional MMO techniques.
Keywords:
KNN model classifier
knowledge transfer
multiobjective multitask optimization (MMO)
transfer rank
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