Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
On the Performance Assessment and Comparison of Stochastic Multiobjective Optimizers
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Specification of Genetic Search Directions in Cellular Multi-objective Genetic Algorithms
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
Multiobjective Evolutionary Algorithms and Applications (Advanced Information and Knowledge Processing)
Proceedings of the 10th annual conference on Genetic and evolutionary computation
SEAL '08 Proceedings of the 7th International Conference on Simulated Evolution and Learning
Multiobjective optimization problems with complicated Pareto sets, MOEA/D and NSGA-II
IEEE Transactions on Evolutionary Computation
Evolutionary many-objective optimization by NSGA-II and MOEA/D with large populations
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Computer Networks: The International Journal of Computer and Telecommunications Networking
Performance scaling of multi-objective evolutionary algorithms
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
Substitute distance assignments in NSGA-II for handling many-objective optimization problems
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
Pareto-, aggregation-, and indicator-based methods in many-objective optimization
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
Expensive multiobjective optimization by MOEA/D with Gaussian process model
IEEE Transactions on Evolutionary Computation
Integrating decision space diversity into hypervolume-based multiobjective search
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Simultaneous use of different scalarizing functions in MOEA/D
Proceedings of the 12th annual conference on Genetic and evolutionary computation
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part II
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
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
On the Evolutionary Optimization of Many Conflicting Objectives
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
SEAL'12 Proceedings of the 9th international conference on Simulated Evolution and Learning
Comprehensive Survey of the Hybrid Evolutionary Algorithms
International Journal of Applied Evolutionary Computation
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Recently MOEA/D (multi-objective evolutionary algorithm based on decomposition) was proposed as a high-performance EMO (evolutionary multiobjective optimization) algorithm. MOEA/D has high search ability as well as high computational efficiency. Whereas other EMO algorithms usually do not work well on many-objective problems with four or more objectives, MOEA/D can properly handle them. This is because its scalarizing function-based fitness evaluation scheme can generate an appropriate selection pressure toward the Pareto front without severely increasing the computation load. MOEA/D can also search for well-distributed solutions along the Pareto front using a number of weight vectors with different directions in scalarizing functions. Currently MOEA/D seems to be one of the best choices for multi-objective optimization in various application fields. In this paper, we examine its performance on multi-objective problems with highly correlated objectives. Similar objectives to existing ones are added to two-objective test problems in computational experiments. Experimental results on multi-objective knapsack problems show that the inclusion of similar objectives severely degrades the performance of MOEA/D while it has almost no negative effects on NSGA-II and SPEA2. We also visually examine such an undesirable behavior of MOEA/D using manyobjective test problems with two decision variables.