An extended fuzzy decision variables framework for solving large-scale multiobjective optimization problems

Abstract

In large-scale multiobjective optimization, the huge search space poses a great challenge to the convergence search of existing evolutionary algorithms. A fuzzy decision variables (FDV) framework for large-scale multiobjective optimization involves a complex parameter tuning process, and the convergence efficiency is moderate. Therefore, we propose an extended fuzzy decision variables (EFDV) framework based on linear search and the non-dominated rate (the proportion of non-dominated individuals in the whole population) to solve large-scale multiobjective optimization problems. First, guiding solutions are sampled from the central region to provide more possible search directions in large-scale decision space. In addition, fuzzy evolution or precise evolution is determined according to the non-dominated rate. When the non-dominated rate is low, the convergence of the whole population is relatively poor. Then a higher degree of fuzzy search is carried out, and the individuals with poor convergence are reversed to increase the population's diversity and enhance the effectiveness of searches. Finally, by comparing experiments on several large-scale multiobjective test suites with 500–5000 decision variables, the efficiency of the EFDV is confirmed. According to experimental results, the EFDV can significantly enhance the performance and computing effectiveness of multiobjective optimizers when used for large-scale multiobjective optimization.