In recent years, the field of Transfer Evolutionary Optimization (TrEO) has witnessed substantial growth, fueled by the realization of its profound impact on solving complex problems. Numerous algorithms have emerged to address the challenges posed by transferring knowledge between tasks. However, the recently highlighted ``no free lunch theorem'' in transfer optimization clarifies that no single algorithm reigns supreme across diverse problem types. This paper addresses this conundrum by adopting a benchmarking approach to evaluate the performance of various TrEO algorithms in realistic scenarios. Despite the growing methodological focus on transfer optimization, existing benchmark problems often fall short due to inadequate design, predominantly featuring synthetic problems that lack real-world relevance. This paper pioneers a practical TrEO benchmark suite, integrating problems from the literature categorized based on the three essential aspects of Big Source Task-Instances: volume, variety, and velocity. Our primary objective is to provide a comprehensive analysis of existing TrEO algorithms and pave the way for the development of new approaches to tackle practical challenges. By introducing realistic benchmarks that embody the three dimensions of volume, variety, and velocity, we aim to foster a deeper understanding of algorithmic performance in the face of diverse and complex transfer scenarios. This benchmark suite is poised to serve as a valuable resource for researchers, facilitating the refinement and advancement of TrEO algorithms in the pursuit of solving real-world problems.

翻译：近年来，迁移进化优化（TrEO）领域因认识到其对解决复杂问题的深远影响而显著发展。众多算法被提出以应对跨任务知识迁移带来的挑战。然而，迁移优化中最近强调的"无免费午餐定理"表明，没有单一算法能够在所有问题类型中占据主导地位。本文通过采用基准测试方法评估各类TrEO算法在现实场景中的性能，从而解决这一难题。尽管迁移优化的方法论研究日益增多，但现有基准问题常因设计不足而效果欠佳，主要采用缺乏实际相关性的合成问题。本文开创性地构建了一套实用TrEO基准测试套件，整合了文献中基于大数据源任务实例三个核心维度（容量、多样性与流速）分类的问题。我们的首要目标是全面分析现有TrEO算法，并为开发应对实际挑战的新方法铺平道路。通过引入体现容量、多样性与流速三维度的现实基准问题，我们旨在促进对算法在多样化复杂迁移场景下性能的深入理解。该基准测试套件有望成为研究人员的宝贵资源，推动TrEO算法在解决现实问题中的优化与进步。