一种基于并行分治策略的大规模优化进化算法
A Parallel Divide-and-Conquer based Evolutionary Algorithm for Large-scale Optimization
AI总结：
本文提出一种基于并行分治策略的进化算法，通过同时求解子问题兼顾解质量与并行计算效率，解决了现有方法在并行计算方案下需牺牲解质量的问题。
摘要：
涉及数千个决策变量的大规模优化问题已广泛出现于各类工业领域。作为一种适用于许多实际应用的强大优化工具，进化算法（EAs）在有效且高效地求解新兴的大规模问题方面存在不足。本文提出了一种新颖的基于分治（Divide-and-Conquer，DC）策略的进化算法，该算法不仅能够通过分别求解子问题来产生高质量的解，还能通过同时求解子问题来充分利用并行计算的能力。现有的基于分治策略的进化算法被认为享有与本文所提算法相同的优势，但研究表明，除非以牺牲解的质量为代价进行某些权衡，否则这些算法实际上与并行计算方案并不兼容。
英文摘要：
Large-scale optimization problems that involve thousands of decision variables have extensively arisen from various industrial areas. As a powerful optimization tool for many real-world applications, evolutionary algorithms (EAs) fail to solve the emerging large-scale problems both effectively and efficiently. In this paper, we propose a novel Divide-and-Conquer (DC) based EA that can not only produce high-quality solution by solving sub-problems separately, but also highly utilizes the power of parallel computing by solving the sub-problems simultaneously. Existing DC-based EAs that were deemed to enjoy the same advantages of the proposed algorithm, are shown to be practically incompatible with the parallel computing scheme, unless some trade-offs are made by compromising the solution quality.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
