Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
A computationally efficient evolutionary algorithm for real-parameter optimization
Evolutionary Computation
On the Performance Assessment and Comparison of Stochastic Multiobjective Optimizers
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Multiobjective evolutionary algorithms: classifications, analyses, and new innovations
Multiobjective evolutionary algorithms: classifications, analyses, and new innovations
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Multi-objective genetic algorithms: Problem difficulties and construction of test problems
Evolutionary Computation
Linkage identification by non-monotonicity detection for overlapping functions
Evolutionary Computation
A scalable multi-objective test problem toolkit
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Introduction to Evolutionary Multiobjective Optimization
Multiobjective Optimization
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
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Steady-state selection and efficient covariance matrix update in the multi-objective CMA-ES
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
A favorable weight-based evolutionary algorithm for multiple criteria problems
IEEE Transactions on Evolutionary Computation
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
A modification to MOEA/D-DE for multiobjective optimization problems with complicated Pareto sets
Information Sciences: an International Journal
The lay of the land: a brief survey of problem understanding
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
WCCI'12 Proceedings of the 2012 World Congress conference on Advances in Computational Intelligence
BSTBGA: A hybrid genetic algorithm for constrained multi-objective optimization problems
Computers and Operations Research
Associated and assorted recombination in SBX operator for problems with linkages
SEMCCO'12 Proceedings of the Third international conference on Swarm, Evolutionary, and Memetic Computing
MOEA/D + uniform design: A new version of MOEA/D for optimization problems with many objectives
Computers and Operations Research
Benchmarks for dynamic multi-objective optimisation algorithms
ACM Computing Surveys (CSUR)
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Existing test problems for multi-objective optimization are criticized for not having adequate linkages among variables. In most problems, the Pareto-optimal solutions correspond to a fixed value of certain variables and diversity of solutions comes mainly from a random variation of certain other variables. In this paper, we introduce explicit linkages among variables so as to develop difficult two and multi-objective test problems along the lines of ZDT and DTLZ problems. On a number of such test problems, this paper compares the performance of a number of EMO methodologies having (i) variable-wise versus vector-wise recombination operators and (ii) spatial versus unidirectional recombination operators. Interesting and useful conclusions on the use of above operators are made from the study.