Evolutionary transfer multiobjective optimization (ETMO) has been becoming a hot research topic in the field of evolutionary computation, which is based on the fact that knowledge learning and transfer across the related optimization exercises can improve the efficiency of others. Besides, the potential for transfer optimization is deemed invaluable from the standpoint of human-like problem-solving capabilities where knowledge gather and reuse are instinctive. To promote the research on ETMO, benchmark problems are of great importance to ETMO algorithm analysis, which helps designers or practitioners to understand the merit and demerit better of ETMO algorithms. Therefore, a total number of 40 benchmark functions are proposed in this report, covering diverse types and properties in the case of knowledge transfer, such as various formulation models, various PS geometries and PF shapes, large-scale of variables, dynamically changed environment, and so on. All the benchmark functions have been implemented in JAVA code, which can be downloaded on the following website: https://github.com/songbai-liu/etmo.
翻译:进化转移多目标优化(ETMO)已成为进化计算领域一个热门研究课题,其基础是在整个相关的优化工作中知识学习和转让能够提高其他功能的效率,此外,从知识收集和再利用具有本能的类似人类解决问题能力的观点来看,转移优化的潜力被认为是非常宝贵的。为促进对ETMO的研究,基准问题对ETMO算法分析非常重要,它帮助设计师或从业人员更好地了解ETMO算法的优点和失色。因此,本报告总共提出了40项基准功能,涉及知识转让的各种类型和特性,例如各种配制模型、各种PS地理特征和PF形状、大规模变量、动态变化环境等等。所有基准功能已在JAVA代码中实施,可在以下网站上下载:https://github.com/songbai-liu/etmo。