1 research outputs found
Effects of Discretization of Decision and Objective Spaces on the Performance of Evolutionary Multiobjective Optimization Algorithms
Recently, the discretization of decision and objective spaces has been
discussed in the literature. In some studies, it is shown that the decision
space discretization improves the performance of evolutionary multi-objective
optimization (EMO) algorithms on continuous multi-objective test problems. In
other studies, it is shown that the objective space discretization improves the
performance on combinatorial multi-objective problems. However, the effect of
the simultaneous discretization of both spaces has not been examined in the
literature. In this paper, we examine the effects of the decision space
discretization, objective space discretization and simultaneous discretization
on the performance of NSGA-II through computational experiments on the DTLZ and
WFG problems. Using various settings about the number of decision variables and
the number of objectives, our experiments are performed on four types of
problems: standard problems, large-scale problems, many-objective problems, and
large-scale many-objective problems. We show that the decision space
discretization has a positive effect for large-scale problems and the objective
space discretization has a positive effect for many-objective problems. We also
show the discretization of both spaces is useful for large-scale many-objective
problems.Comment: 2019 IEEE Symposium Series on Computational Intelligence (SSCI