Convergence versus diversity in multiobjective optimization

Date

2016-08-31

Advisors

Journal Title

Journal ISSN

ISSN

Volume Title

Publisher

Springer

Type

Conference

Peer reviewed

Yes

Abstract

Convergence and diversity are two main goals in multiobjective optimization. In literature, most existing multiobjective optimization evolutionary algorithms (MOEAs) adopt a convergence-first-and-diversity-second environmental selection which prefers nondominated solutions to dominated ones, as is the case with the popular nondominated sorting based selection method. While convergence-first sorting has continuously shown effectiveness for handling a variety of problems, it faces challenges to maintain well population diversity due to the overemphasis of convergence. In this paper, we propose a general diversity-first sorting method for multiobjective optimization. Based on the method, a new MOEA, called DBEA, is then introduced. DBEA is compared with the recently-developed nondominated sorting genetic algorithm III (NSGA-III) on different problems. Experimental studies show that the diversity-first method has great potential for diversity maintenance and is very competitive for many-objective optimization.

Description

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

Keywords

Multiobjective optimization problems, Evolutionary Computation, Convergence, Diversity

Citation

Jiang, S. and Yang, S. (2016) Convergence versus diversity in multiobjective optimization. Proceedings of the 14th International Conference on Parallel Problems Solving from Nature (PPSN XIV), Lecture Notes in Computer Science, 9921, pp. 984-993

Rights

Research Institute

Institute of Artificial Intelligence (IAI)