透過張量化架構連接演化多目標最佳化與GPU加速

Bridging Evolutionary Multiobjective Optimization and GPU Acceleration via Tensorization

March 26, 2025

作者: Zhenyu Liang, Hao Li, Naiwei Yu, Kebin Sun, Ran Cheng

cs.AI

摘要

在過去的二十年中，進化多目標優化（EMO）取得了顯著的進展。然而，隨著問題規模和複雜性的增加，傳統的EMO算法由於並行性和可擴展性不足而面臨顯著的性能限制。雖然大多數工作都集中在算法設計以應對這些挑戰，但對硬件加速的關注卻很少，這使得EMO算法與先進計算設備（如GPU）之間存在明顯的差距。為了彌補這一差距，我們提出通過張量化方法在GPU上並行化EMO算法。通過採用張量化，EMO算法的數據結構和操作被轉換為簡潔的張量表示，從而無縫地實現GPU計算的自動利用。我們通過將其應用於三個具有代表性的EMO算法：NSGA-III、MOEA/D和HypE，展示了我們方法的有效性。為了全面評估我們的方法，我們引入了一個使用GPU加速物理引擎的多目標機器人控制基準。我們的實驗表明，與基於CPU的對應算法相比，張量化EMO算法實現了高達1113倍的加速，同時保持了解決方案的質量，並有效地將種群規模擴展到數十萬。此外，張量化EMO算法高效地處理了複雜的多目標機器人控制任務，產生了具有多樣行為的高質量解決方案。源代碼可在https://github.com/EMI-Group/evomo獲取。

English

Evolutionary multiobjective optimization (EMO) has made significant strides over the past two decades. However, as problem scales and complexities increase, traditional EMO algorithms face substantial performance limitations due to insufficient parallelism and scalability. While most work has focused on algorithm design to address these challenges, little attention has been given to hardware acceleration, thereby leaving a clear gap between EMO algorithms and advanced computing devices, such as GPUs. To bridge the gap, we propose to parallelize EMO algorithms on GPUs via the tensorization methodology. By employing tensorization, the data structures and operations of EMO algorithms are transformed into concise tensor representations, which seamlessly enables automatic utilization of GPU computing. We demonstrate the effectiveness of our approach by applying it to three representative EMO algorithms: NSGA-III, MOEA/D, and HypE. To comprehensively assess our methodology, we introduce a multiobjective robot control benchmark using a GPU-accelerated physics engine. Our experiments show that the tensorized EMO algorithms achieve speedups of up to 1113x compared to their CPU-based counterparts, while maintaining solution quality and effectively scaling population sizes to hundreds of thousands. Furthermore, the tensorized EMO algorithms efficiently tackle complex multiobjective robot control tasks, producing high-quality solutions with diverse behaviors. Source codes are available at https://github.com/EMI-Group/evomo.