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yang
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xu

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In large-scale multiobjective optimization, too many decision variables hinder the convergence search of evolutionary algorithms. Reducing the search range of the decision space will significantly alleviate this puzzle. With this in mind, this paper proposes a fuzzy decision variables framework for large-scale multiobjective optimization. The framework divides the entire evolutionary process into two main stages: fuzzy evolution and precise evolution.

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In large-scale multi-objective optimization, as the decision space's dimensionality increases, evolutionary algorithms can easily fall into an optimal local state. Therefore, how to prevent the algorithm from falling into a local optimum and quickly converge to the Pareto front is a particularly challenging problem. In order to solve the problem, this paper proposes a grid-based fuzzy evolution large-scale multi-objective optimization framework, which divides the entire evolution process into two main stages: fuzzy evolution and precise evolution.

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