EmoPyLab: A Tensor-Native, Hardware-Accelerated Laboratory for High-Throughput Benchmarking and Decision-Making in Multi/Many-Objective Optimization

Abstract

This paper presents \textit{EmoPyLab}, an open-source, tensor-native Python framework for high-throughput benchmarking, analysis, and multi-criteria decision-making in multi- and many-objective optimization ($M\ge 2$). Traditional evolutionary toolchains rely on object-oriented Array-of-Structures (AoS) models that incur memory fragmentation and interpreter overhead during large campaigns, while emerging tensor engines focus primarily on single-objective neuroevolution, lacking high-dimensional analytics and post-Pareto decision support. EmoPyLab resolves this dichotomy via a contiguous Structure-of-Arrays (SoA) columnar data model executed across a 5-tier backend dispatch engine (NVIDIA CUDA, Apple MLX, JAX, CuPy, and CPU SIMD) with zero-copy persistence. The platform incorporates deductive Efficient Non-dominated Sorting ($O(MN\sqrt{N})$) and a low-discrepancy quasi-Monte Carlo Hypervolume estimator (Fast-MC HV) with Sobol sequence pruning, enabling real-time analytical evaluation in up to 15-objective spaces. Alongside 298+ metaheuristics and 916 benchmark instances, EmoPyLab embeds an in-situ Multi-Criteria Decision Making (MCDM) layer (TOPSIS, PROMETHEE II, and Compromise Programming) with decision-space back-mapping, automated non-parametric statistics (Wilcoxon, Friedman with Kendall's $W$, Vargha-Delaney $A_{12}$, Holm-Bonferroni correction), and cryptographic SHA-256 reproducibility manifests. The ecosystem provides both a memory-safe reactive graphical workspace ($<150\text{ MB}$ RAM) and a headless CLI for distributed supercomputers. Empirical validation across 5,970 runs on the Santos Dumont Bull Sequana supercomputer confirms linear multi-core scaling and robust thread-oversubscription suppression, establishing a unified, reproducible, hardware-accelerated laboratory under the MIT license.