Performance of Decomposition-Based Many-Objective Algorithms Strongly Depends on Pareto Front Shapes

Recently, a number of high performance many-objective evolutionary algorithms with systematically generated weight vectors have been proposed in the literature. Those algorithms often show surprisingly good performance on widely used DTLZ and WFG test problems. The performance of those algorithms ha...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:IEEE transactions on evolutionary computation Jg. 21; H. 2; S. 169 - 190
Hauptverfasser: Ishibuchi, Hisao, Setoguchi, Yu, Masuda, Hiroyuki, Nojima, Yusuke
Format: Journal Article
Sprache:Englisch
Veröffentlicht: IEEE 01.04.2017
Schlagworte:
Convergence
Decomposition-based evolutionary algorithms
Evolutionary computation
Maintenance engineering
many-objective evolutionary algorithms
many-objective optimization
many-objective test problems
Optimization
Performance evaluation
Search problems
Shape
ISSN:1089-778X, 1941-0026
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Recently, a number of high performance many-objective evolutionary algorithms with systematically generated weight vectors have been proposed in the literature. Those algorithms often show surprisingly good performance on widely used DTLZ and WFG test problems. The performance of those algorithms has continued to be improved. The aim of this paper is to show our concern that such a performance improvement race may lead to the overspecialization of developed algorithms for the frequently used many-objective test problems. In this paper, we first explain the DTLZ and WFG test problems. Next, we explain many-objective evolutionary algorithms characterized by the use of systematically generated weight vectors. Then we discuss the relation between the features of the test problems and the search mechanisms of weight vector-based algorithms such as multiobjective evolutionary algorithm based on decomposition (MOEA/D), nondominated sorting genetic algorithm III (NSGA-III), MOEA/dominance and decomposition (MOEA/DD), and θ-dominance based evolutionary algorithm (θ-DEA). Through computational experiments, we demonstrate that a slight change in the problem formulations of DTLZ and WFG deteriorates the performance of those algorithms. After explaining the reason for the performance deterioration, we discuss the necessity of more general test problems and more flexible algorithms.
ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2016.2587749