Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Multi-objective genetic algorithms: Problem difficulties and construction of test problems
Evolutionary Computation
Solving rotated multi-objective optimization problems using differential evolution
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Properties of an adaptive archiving algorithm for storing nondominated vectors
IEEE Transactions on Evolutionary Computation
Performance assessment of multiobjective optimizers: an analysis and review
IEEE Transactions on Evolutionary Computation
The balance between proximity and diversity in multiobjective evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Combine LHS with MOEA to Optimize Complex Pareto Set MOPs
ISICA '08 Proceedings of the 3rd International Symposium on Advances in Computation and Intelligence
Multiobjective optimization problems with complicated Pareto sets, MOEA/D and NSGA-II
IEEE Transactions on Evolutionary Computation
Enhancing MOEA/D with guided mutation and priority update for multi-objective optimization
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Recent advances in differential evolution: a survey and experimental analysis
Artificial Intelligence Review
Prediction-based population re-initialization for evolutionary dynamic multi-objective optimization
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
The RM-MEDA based on elitist strategy
ISICA'10 Proceedings of the 5th international conference on Advances in computation and intelligence
Effects of the existence of highly correlated objectives on the behavior of MOEA/D
EMO'11 Proceedings of the 6th international conference on Evolutionary multi-criterion optimization
Framework for many-objective test problems with both simple and complicated pareto-set shapes
EMO'11 Proceedings of the 6th international conference on Evolutionary multi-criterion optimization
Advances in differential evolution for solving multiobjective optimization problems
ICSI'12 Proceedings of the Third international conference on Advances in Swarm Intelligence - Volume Part I
Benchmarks for dynamic multi-objective optimisation algorithms
ACM Computing Surveys (CSUR)
An analysis on separability for Memetic Computing automatic design
Information Sciences: an International Journal
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Although a number of multiobjective evolutionary algorithms have been proposed over the last two decades, not much effort has been made to deal with variable linkages in multiobjective optimization. Recently, we have suggested a general framework of multiobjective evolutionary algorithms based on decomposition (MOEA/D) [1]. MOEA/D decomposes a MOP into a number of scalar optimization subproblems by a conventional decomposition method. The optimal solution to each of these problems is a Pareto optimal solution to the MOP under consideration. An appropriate decomposition could make these individual Pareto solutions evenly distribute along the Pareto optimal front. MOEA/D aims at solving these scalar optimization subproblems simultaneously. In this paper, we propose, under the framework of MOEA/D, a multiobjective differential evolution based decomposition (MODE/D) for tackling variable linkages. Our experimental results show that MODE/D outperforms several other MOEAs on several test problems with variable linkages.