A fuzzy decision variables framework for large-scale multiobjective optimization





Journal Title

Journal ISSN



Volume Title


IEEE Press



Peer reviewed



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 largescale multiobjective optimization. The framework divides the entire evolutionary process into two main stages: fuzzy evolution and precise evolution. In fuzzy evolution, we blur the decision variables of the original solution to reduce the search range of the evolutionary algorithm in the decision space so that the evolutionary population can quickly converge. The degree of fuzzification gradually decreases with the evolutionary process. Once the population approximately converges, the framework will turn to precise evolution. In precise evolution, the actual decision variables of the solution are directly optimized to increase the diversity of the population so as to be closer to the true Pareto optimal front. Finally, this paper embeds some representative algorithms into the proposed framework and verifies the framework’s effectiveness through comparative experiments on various large-scale multiobjective problems with 500 to 5000 decision variables. Experimental results show that in large-scale multiobjective optimization, the framework proposed in this paper can significantly improve the performance and computational efficiency of multiobjective optimization algorithms.


The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.


Evolutionary algorithms, Large-scale optimization, Multiobjective optimization, Fuzzy evolution, Decision variable


Yang, X., Zou, J., Yang, S., Zheng, J. and Liu, Y. (2021) A fuzzy decision variables framework for large-scale multiobjective optimization. IEEE Transactions on Evolutionary Computation. 27 (3), pp. 445-459


Research Institute

Institute of Artificial Intelligence (IAI)