MAEO: Multiobjective Animorphic Ensemble Optimization for Scalable Large-scale Engineering Applications
Multiobjective optimization remains challenging for many scientific and engineering problems due to the need to balance convergence, diversity, and computational efficiency across high-dimensional objective landscapes. This work presents the Multiobjective Animorphic Ensemble Optimization (MAEO) framework, a parallelizable ensemble strategy that unifies state-of-the-art evolutionary algorithms within an island-based architecture, overcoming the limitations of relying on a single optimizer, as implied by the No Free Lunch theorem. MAEO uses a parameter-free hypervolume indicator for island performance assessment and a strict Pareto-rank-based individual scoring formulation that incorporate...
| Authors | Omer F. Erdem, Dean Price, Paul Seurin et al. |
|---|---|
| Primary category | cs.NE |
| Published | 26 April 2026 |
| Last updated at source | 26 April 2026 |
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