Convergence versus diversity in multiobjective optimization
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.
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
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
ISBN : 9783319458229
Research Group : Centre for Computational Intelligence
Research Institute : Institute of Artificial Intelligence (IAI)
Peer Reviewed : Yes