AI RESEARCH
MAEO: Multiobjective Animorphic Ensemble Optimization for Scalable Large-scale Engineering Applications
arXiv CS.LG
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ArXi:2604.26973v1 Announce Type: cross 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.