Convergence and diversity are two main goals in multiobjective optimization. In literature, most existing multiobjective optimization evolutionary algorithms (MOEAs) adopt a convergencefirst- 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.
|Title of host publication||Parallel Problem Solving from Nature - 14th International Conference, PPSN 2016, Proceedings|
|Editors||Emma Hart, Ben Paechter, Julia Handl, Manuel López-Ibáñez, Peter R. Lewis, Gabriela Ochoa|
|Number of pages||10|
|Publication status||Published - 2016|
|Event||14th International Conference on Parallel Problem Solving from Nature, PPSN 2016 - Edinburgh, United Kingdom|
Duration: 17 Sept 2016 → 21 Sept 2016
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||14th International Conference on Parallel Problem Solving from Nature, PPSN 2016|
|Period||17/09/16 → 21/09/16|
Bibliographical noteFunding Information:
This work was funded by the Engineering and Physical Sciences Research Council (EPSRC) of U.K. under Grant EP/K001310/1.
© Springer International Publishing AG 2016.