Evolutionary multitasking (EMT) has emerged as a popular topic of evolutionary computation over the past decade. It aims to concurrently address multiple optimization tasks within limited computing resources, leveraging inter-task knowledge transfer techniques. Despite the abundance of multitask evolutionary algorithms (MTEAs) proposed for multitask optimization (MTO), there remains a need for a comprehensive software platform to help researchers evaluate MTEA performance on benchmark MTO problems as well as explore real-world applications. To bridge this gap, we introduce the first open-source benchmarking platform, named MToP, for EMT. MToP incorporates over 50 MTEAs, more than 200 MTO problem cases with real-world applications, and over 20 performance metrics. Based on these, we provide benchmarking recommendations tailored for different MTO scenarios. Moreover, to facilitate comparative analyses between MTEAs and traditional evolutionary algorithms, we adapted over 50 popular single-task evolutionary algorithms to address MTO problems. Notably, we release extensive pre-run experimental data on benchmark suites to enhance reproducibility and reduce computational overhead for researchers. MToP features a user-friendly graphical interface, facilitating results analysis, data export, and schematic visualization. More importantly, MToP is designed with extensibility in mind, allowing users to develop new algorithms and tackle emerging problem domains. The source code of MToP is available at: https://github.com/intLyc/MTO-Platform

翻译：进化多任务优化（EMT）在过去十年中已成为进化计算领域的热点研究方向。该技术旨在通过任务间知识迁移方法，在有限计算资源下同时处理多个优化任务。尽管已有大量针对多任务优化（MTO）提出的多任务进化算法（MTEA），学界仍缺乏一个完整的软件平台来帮助研究人员评估MTEA在基准MTO问题上的性能并探索实际应用。为填补这一空白，我们推出了首个面向EMT的开源基准测试平台MToP。该平台整合了50余种MTEA算法、200多个涵盖实际应用的MTO问题案例以及20余项性能评价指标。基于这些资源，我们针对不同MTO场景提供了定制化的基准测试建议。此外，为促进MTEA与传统进化算法的对比分析，我们适配了50多种经典单任务进化算法以处理MTO问题。值得注意的是，我们发布了基准测试套件上大量预运行的实验数据，以提升研究可复现性并降低计算开销。MToP配备友好的图形界面，支持结果分析、数据导出与示意图可视化。更重要的是，平台采用可扩展架构设计，允许用户开发新算法并应对新兴问题领域。MToP源代码已发布于：https://github.com/intLyc/MTO-Platform