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 incorporates crowding distance and nadir-point proximity to ensure consistent selection pressure within each front. The framework is initiated using four algorithms (NSGA-III, CTAEA, AGEMOEA2, SPEA2) and evaluated through extensive benchmarking on 12 DTLZ/ZDT functions under 36 dimensionality settings using Wilcoxon signed-rank tests with both hypervolume and inverse generational distance metrics. Results show that MAEO achieves balanced convergence-diversity performance, outperforming or matching some of the leading multiobjective optimization algorithms across different benchmark problems. To demonstrate practical applicability, MAEO is applied to the equilibrium-cycle optimization of a small modular nuclear reactor. Eight discrete design variables (and three objectives (levelized cost of electricity, peak soluble boron concentration, fuel cycle length) are optimized under two safety constraints. The algorithm carried out roughly 40000 evaluations using computer simulations. MAEO identifies core designs that lower both the levelized cost of electricity and the peak boron concentration, while preserving fuel cycle length and meeting all safety constraints.

翻译：多目标优化在众多科学与工程问题中仍面临挑战，这源于需在高维目标景观中平衡收敛性、多样性与计算效率的需求。本文提出多目标形变集成优化（MAEO）框架，这是一种可并行化的集成策略，通过岛屿架构统一了最先进的进化算法，克服了“没有免费午餐”定理所暗示的依赖单一优化器的局限性。MAEO采用无参数超体积指标评估岛屿性能，并基于严格帕累托秩的个体评分公式，该公式融合了拥挤距离与纳迪尔点邻近度，以确保各前沿内部的选择压力一致性。该框架使用四种算法（NSGA-III、CTAEA、AGEMOEA2、SPEA2）作为初始化，并在36种维度设置下对12个DTLZ/ZDT函数进行广泛基准测试，采用Wilcoxon符号秩检验与超体积及逆世代距离指标进行评估。结果表明，MAEO实现了收敛性与多样性的平衡，在不同基准问题上优于或匹配部分领先的多目标优化算法。为展示其实用性，MAEO被应用于小型模块化核反应堆的平衡循环优化。在两项安全约束下，对八个离散设计变量与三个目标（平准化电力成本、峰值可溶硼浓度、燃料循环长度）进行优化。该算法通过计算机仿真执行了约40000次评估。MAEO识别出的堆芯设计在保持燃料循环长度并满足所有安全约束的同时，降低了平准化电力成本与峰值硼浓度。