Multi-objective multi-criteria evolutionary algorithm for multi-objective multi-task optimization

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Du, Ke-Jing, Li, Jian-Yu ORCID: 0000-0002-6143-9207, Wang, Hua ORCID: 0000-0002-8465-0996 and Zhang, Jun ORCID: 0000-0001-7835-9871 (2022) Multi-objective multi-criteria evolutionary algorithm for multi-objective multi-task optimization. Complex and Intelligent Systems, 9. pp. 1211-1228. ISSN 2199-4536


Evolutionary multi-objective multi-task optimization is an emerging paradigm for solving multi-objective multi-task optimization problem (MO-MTOP) using evolutionary computation. However, most existing methods tend to directly treat the multiple multi-objective tasks as different problems and optimize them by different populations, which face the difficulty in designing good knowledge transferring strategy among the tasks/populations. Different from existing methods that suffer from the difficult knowledge transfer, this paper proposes to treat the MO-MTOP as a multi-objective multi-criteria optimization problem (MO-MCOP), so that the knowledge of all the tasks can be inherited in a same population to be fully utilized for solving the MO-MTOP more efficiently. To be specific, the fitness evaluation function of each task in the MO-MTOP is treated as an evaluation criterion in the corresponding MO-MCOP, and therefore, the MO-MCOP has multiple relevant evaluation criteria to help the individual selection and evolution in different evolutionary stages. Furthermore, a probability-based criterion selection strategy and an adaptive parameter learning method are also proposed to better select the fitness evaluation function as the criterion. By doing so, the algorithm can use suitable evaluation criteria from different tasks at different evolutionary stages to guide the individual selection and population evolution, so as to find out the Pareto optimal solutions of all tasks. By integrating the above, this paper develops a multi-objective multi-criteria evolutionary algorithm framework for solving MO-MTOP. To investigate the proposed algorithm, extensive experiments are conducted on widely used MO-MTOPs to compare with some state-of-the-art and well-performing algorithms, which have verified the great effectiveness and efficiency of the proposed algorithm. Therefore, treating MO-MTOP as MO-MCOP is a potential and promising direction for solving MO-MTOP.

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Item type Article
DOI 10.1007/s40747-022-00650-8
Official URL
Subjects Current > FOR (2020) Classification > 4602 Artificial intelligence
Current > Division/Research > Institute for Sustainable Industries and Liveable Cities
Keywords optimisation tasks, multi task optimisation, algorithm, evolutionary algorithm
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