Performance assessment of DMOEA-DD with CEC 2009 MOEA competition test instances

Authors:
Minzhong Liu;Xiufen Zou;Yu Chen;Zhijian Wu
Affiliations:
School of Computer Sciences, Wuhan University, Wuhan, China;School of Mathematics and Statistics, Wuhan University, Wuhan, China;School of Mathematics and Statistics, Wuhan University, Wuhan, China;State Key Laboratory of Software Engineering, Wuhan University, Wuhan, China
Venue:
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Year:
2009

Citing 4
Cited 5

Multiobjective optimization problems with complicated Pareto sets, MOEA/D and NSGA-II

IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II

IEEE Transactions on Evolutionary Computation
MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition

IEEE Transactions on Evolutionary Computation
A New Evolutionary Algorithm for Solving Many-Objective Optimization Problems

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

Multi-Pareto-Ranking evolutionary algorithm

EvoCOP'12 Proceedings of the 12th European conference on Evolutionary Computation in Combinatorial Optimization
Adaptive multi-objective genetic algorithm using multi-pareto-ranking

Proceedings of the 14th annual conference on Genetic and evolutionary computation
Hybridisation of real-code population-based incremental learning and differential evolution for multiobjective design of trusses

Information Sciences: an International Journal
A study of two penalty-parameterless constraint handling techniques in the framework of MOEA/D

Applied Soft Computing
Multi Agent Collaborative Search based on Tchebycheff decomposition

Computational Optimization and Applications

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, the DMOEA-DD, which is an improvement of DMOEA[1, 2] by using domain decomposition technique, is applied to tackle the CEC 2009 MOEA competition test instances that are multiobjective optimization problems (MOPs) with complicated Pareto set (PS) geometry shapes. The performance assessment is given by using IGD [3, 4] as performance metric.