Multiobjective Optimization Using Evolutionary Algorithms - A Comparative Case Study
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
A universal mapping for kolmogorov's superposition theorem
Neural Networks
Expensive multiobjective optimization by MOEA/D with Gaussian process model
IEEE Transactions on Evolutionary Computation
A memetic algorithm with non gradient-based local search assisted by a meta-model
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
A multi-objective particle swarm optimizer based on decomposition
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Performance assessment of multiobjective optimizers: an analysis and review
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Single- and multiobjective evolutionary optimization assisted by Gaussian random field metamodels
IEEE Transactions on Evolutionary Computation
MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition
IEEE Transactions on Evolutionary Computation
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The development of multi-objective evolutionary algorithms assisted by surrogate models has increased in the last few years. However, in real-world applications, the high modality and dimensionality that functions have, often causes problems to such models. In fact, if the Pareto optimal set of a multi-objective optimization problem is located in a search space in which the surrogate model is not able to shape the corresponding region, the search could be misinformed and thus converge to wrong regions. Because of this, a considerable amount of research has focused on improving the prediction of the surrogate models by adding the new solutions to the training set and retraining the model. However, when the size of the training set increases, the training complexity can significantly increase. In this paper, we present a surrogate model which maintains the size of the training set, and in which the prediction of the function is improved by using radial basis function networks in a cooperative way. Preliminary results indicate that our proposed approach can produce good quality results when it is restricted to performing only 200, 1,000 and 5,000 fitness function evaluations. Our proposed approach is validated using a set of standard test problems and an airfoil design problem.