Evolutionary Algorithms for Solving Multi-Objective Problems
Evolutionary Algorithms for Solving Multi-Objective Problems
Combining convergence and diversity in evolutionary multiobjective optimization
Evolutionary Computation
Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization
Proceedings of the 5th International Conference on Genetic Algorithms
Evolutionary Search for Minimal Elements in Partially Ordered Finite Sets
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Scalable Model-based Clustering by Working on Data Summaries
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Expanding self-organizing map for data visualization and cluster analysis
Information Sciences: an International Journal - Special issue: Soft computing data mining
Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy
Evolutionary Computation
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
Towards a quick computation of well-spread pareto-optimal solutions
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
Trade-off between performance and robustness: an evolutionary multiobjective approach
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Properties of an adaptive archiving algorithm for storing nondominated vectors
IEEE Transactions on Evolutionary Computation
Dynamic multiobjective evolutionary algorithm: adaptive cell-based rank and density estimation
IEEE Transactions on Evolutionary Computation
Using unconstrained elite archives for multiobjective optimization
IEEE Transactions on Evolutionary Computation
Handling multiple objectives with particle swarm optimization
IEEE Transactions on Evolutionary Computation
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It is crucial to obtain automatically and efficiently a well-distributed set of Pareto optimal solutions in multiobjective evolutionary algorithms (MOEAs). Many studies have proposed different evolutionary algorithms that can progress toward the Pareto front with a widely spread distribution of solutions. However, most theoretically, convergent MOEAs necessitate certain prior knowledge about the Pareto front in order to efficiently maintain widespread solutions. In this paper, we propose, based on the new E-dominance concept, an Adaptive Rectangle Archiving (ARA) strategy that overcomes this important problem. The MOEA with this archiving technique provably converges to well-distributed Pareto optimal solutions without prior knowledge about the Pareto front. ARA complements the existing archiving techniques and is useful to both researchers and practitioners.