LAUSR

This paper proposes a novel master–slave parallel evolutionary algorithm (EA) approach with different asynchrony and provides its detailed analyses on multi-objective optimization problems. We express the proposed EA with different asynchrony as a semi-asynchronous EA. A semi-asynchronous EA generates new solutions whenever evaluations of the predefined number of solutions complete, unlike a conventional synchronous EA waits for evaluations of all solutions to generate the next population. To establish a semi-asynchronous EA, this paper introduces an asynchrony parameter that is used to decide how many solutions are waited to generate new solutions. We conduct an experiment to verify the effectiveness of the proposed semi-asynchronous EA on benchmark problems with the several variances of the evaluation time. In the experiment, we apply a semi-asynchronous EA to NSGA-II and NSGA-III, which are well-known multi-objective EAs. The semi-asynchronous NSGA-IIs and the semi-asynchronous NSGA-IIIs with different asynchronies are compared on multi-objective optimization benchmark problems. The experimental result reveals that semi-asynchronous approaches with an appropriate asynchrony have possibility to outperform the asynchronous and the synchronous ones. Additionally, detailed analysis reveals that an appropriate asynchrony may vary not only depends on a target problem but also depends on the degree of the evolution process.