Modeling of dynamic systems
System identification (2nd ed.): theory for the user
System identification (2nd ed.): theory for the user
Multi-Objective Optimization using Grid Computing
Soft Computing - A Fusion of Foundations, Methodologies and Applications
An informed convergence accelerator for evolutionary multiobjective optimiser
Proceedings of the 9th annual conference on Genetic and evolutionary computation
An analysis of the effects of population structure on scalable multiobjective optimization problems
Proceedings of the 9th annual conference on Genetic and evolutionary computation
A pareto following variation operator for fast-converging multiobjective evolutionary algorithms
Proceedings of the 10th annual conference on Genetic and evolutionary computation
On the performance of metamodel assisted MOEA/D
ISICA'07 Proceedings of the 2nd international conference on Advances in computation and intelligence
Parallelization of multi-objective evolutionary algorithms using clustering algorithms
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
Considerations in engineering parallel multiobjective evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Dynamic multiobjective optimization problems: test cases, approximations, and applications
IEEE Transactions on Evolutionary Computation
Single- and multiobjective evolutionary optimization assisted by Gaussian random field metamodels
IEEE Transactions on Evolutionary Computation
The Pareto-following variation operator as an alternative approximation model
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Benchmarks for dynamic multi-objective optimisation algorithms
ACM Computing Surveys (CSUR)
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One of the major difficulties when applying Multiobjective Evolutionary Algorithms (MOEA) to real world problems is the large number of objective function evaluations. Approximate (or surrogate) methods offer the possibility of reducing the number of evaluations, without reducing solution quality. Artificial Neural Network (ANN) based models are one approach that have been utilized to approximate the future front from the current available fronts with acceptable accuracy levels. However, the associated computational costs limit their effectiveness. In this research project, we have developed a simple approximation technique with comparatively smaller computational cost. Our model, has been developed as a variation operator that can be utilized in any kind of multiobjective optimizer. Initial simulation experiments have produced encouraging results in comparison to other existing sequential algorithms (i.e. NSGA-II, SPEA-II). In the next phase of the project, this model will be integrated into other existing parallel MOEA's to solve more complex and time intensive bench mark problems.