Privacy-Enhanced Offline Data-Driven Evolutionary Optimization Based on Cloud Server
针对用户缺乏专业知识和计算资源的问题，提出进化学习与优化即服务（ELOaaS）范式，并设计隐私增强的离线数据驱动进化算法（PEDDEA），通过子空间学习保护隐私、Kendall tau指标构建高质量代理模型，在基准问题和自动驾驶安全评估上验证了性能优势。
Data-driven evolutionary algorithms (DDEAs) have achieved significant success in numerous real-world optimization problems, where exact objective functions and constraint functions do not exist, and they mainly rely on available data. However, the existing DDEAs primarily focus on improving performance through data and surrogate, without considering that the users may lack the specialized domain knowledge and sufficient computing resources required for DDEAs. To address the aforementioned issues, this paper proposes a novel paradigm called Evolutionary Learning and Optimization as a Service (ELOaaS) and investigates the potential collusion attacks between machine learning modules and evolutionary computing modules on cloud server, which may lead to privacy leakage. Consequently, a privacy-enhanced DDEA (PEDDEA) is proposed as an instantiation algorithm of ELOaaS, which is designed to tackle offline data-driven evolutionary optimization within the ELOaaS paradigm. In the proposed PEDDEA, a subspace learning-based privacy protection strategy is designed to defense the collusion attacks. Additionally, a model management strategy based on Kendall tau metric is introduced to construct high-quality surrogate ensembles. PEDDEA enables users to outsource private offline data to cloud servers, thereby approaching the optimal solution while ensuring privacy protection. Comprehensive experiments are conducted on benchmark problems and safety evaluation problems of autonomous vehicles. According to the experimental results, the proposed algorithm has significant performance advantages over existing offline DDEAs while ensuring privacy protection.