Introduces a single-number performance measure, file-based benchmarking, and efficient text-file storage to evaluate and compare stopping criteria for EMO algorithms.
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cs.NE 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
MAEO is a new ensemble framework that runs NSGA-III, CTAEA, AGEMOEA2 and SPEA2 in parallel islands with parameter-free hypervolume assessment and strict Pareto-rank selection, showing competitive or better results on DTLZ/ZDT benchmarks and identifying improved nuclear reactor designs.
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Benchmarking Stopping Criteria for Evolutionary Multi-objective Optimization
Introduces a single-number performance measure, file-based benchmarking, and efficient text-file storage to evaluate and compare stopping criteria for EMO algorithms.
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MAEO: Multiobjective Animorphic Ensemble Optimization for Scalable Large-scale Engineering Applications
MAEO is a new ensemble framework that runs NSGA-III, CTAEA, AGEMOEA2 and SPEA2 in parallel islands with parameter-free hypervolume assessment and strict Pareto-rank selection, showing competitive or better results on DTLZ/ZDT benchmarks and identifying improved nuclear reactor designs.