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Learning to optimize by multi-gradient for multi-objective optimization
The development of artificial intelligence (AI) for science has led to the emergence of learning-based research paradigms, necessitating a compelling reevaluation of the design of multi-objective optimization (MOO) methods.
A novel multiobjective evolutionary algorithm based on decomposition and multi-reference points strategy
For performance assessment, the proposed algorithm is compared with existing four state-of-the-art multiobjective evolutionary algorithms on benchmark test problems with various types of Pareto optimal fronts.
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