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)
Parallel Computing - Optimization on grids - Optimization for grids
Parallel Approaches for Multiobjective Optimization
Multiobjective Optimization
Parallel multi-objective evolutionary algorithms on graphics processing units
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
A specialized island model and its application in multiobjective optimization
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Distributed computing of Pareto-optimal solutions with evolutionary algorithms
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
ISICA'07 Proceedings of the 2nd international conference on Advances in computation and intelligence
On the impact of the migration topology on the Island Model
Parallel Computing
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Parallelism and evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Reducing the run-time complexity of multiobjective EAs: The NSGA-II and other algorithms
IEEE Transactions on Evolutionary Computation
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This work presents an implementation of the asynchronous island model suitable for multi-objective evolutionary optimization on heterogeneous and large-scale computing platforms. The migration of individuals is regulated by the crowding comparison operator applied to the originating population during selection and to the receiving population augmented by all migrants during replacement. Experiments using this method combined with NSGA-II show its scalability up to 128 islands and its robustness. Furthermore, the proposed parallelization technique consistently outperforms a multi-start and a random migration approach in terms of convergence speed, while maintaining a comparable population diversity. Applied to a real-world problem of interplanetary trajectory design, we find solutions dominating an actual NASA/ESA mission proposal for a tour from Earth to Jupiter, in a fraction of the computational time that would be needed on a single CPU.