An introduction to differential evolution
New ideas in optimization
Evolutionary Algorithms for Solving Multi-Objective Problems
Evolutionary Algorithms for Solving Multi-Objective Problems
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Constraint Method-Based Evolutionary Algorithm (CMEA) for Multiobjective Optimization
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
Knowledge-based solution to dynamic optimization problems using cultural algorithms
Knowledge-based solution to dynamic optimization problems using cultural algorithms
Optimization with constraints using a cultured differential evolution approach
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Minimal sets of quality metrics
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
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
Epsilon-constraint with an efficient cultured differential evolution
Proceedings of the 9th annual conference companion on Genetic and evolutionary computation
Alternative techniques to solve hard multi-objective optimization problems
Proceedings of the 9th annual conference on Genetic and evolutionary computation
On improving normal boundary intersection method for generation of Pareto frontier
Structural and Multidisciplinary Optimization
Environmental Modelling & Software
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In this paper, we propose the use of a mathematical programming technique called the ε-constraint method, hybridized with an evolutionary single-objective optimizer: the cultured differential evolution. The ε-constraint method uses the cultured differential evolution to produce one point of the Pareto front of a multiobjective optimization problem at each iteration. This approach is able to solve difficult multiobjective problems, relying on the efficiency of the single-objective optimizer, and on the fact that none of the two approaches (the mathematical programming technique or the evolutionary algorithm) are required to generate the entire Pareto front at once. The proposed approach is validated using several difficult multiobjective test problems, and our results are compared with respect to a multi-objective evolutionary algorithm representative of the state-of-the-art in the area: the NSGA-II.