Evolutionary multitasking (EMT) has emerged as a popular topic of evolutionary computation over the past years. 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 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 optimization platform, named MTO-Platform (MToP), for EMT. MToP incorporates over 40 MTEAs, more than 150 MTO problem cases with real-world applications, and over 10 performance metrics. Moreover, to facilitate comparative analyses between MTEAs and traditional evolutionary algorithms, we adapted over 40 popular single-task evolutionary algorithms to address MTO problems. MToP boasts a user-friendly graphical interface, facilitating results analysis, data export, and schematics 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）近年已成为进化计算领域的热点课题，其旨在利用有限计算资源，通过任务间知识迁移技术同时求解多个优化任务。尽管针对多任务优化（MTO）已涌现大量多任务进化算法（MTEA），但目前仍缺乏一个综合性软件平台，以帮助研究人员在基准MTO问题上评估MTEA性能并探索实际应用。为填补这一空白，我们首次提出EMT领域开源优化平台MTO-Platform（MToP）。该平台集成超过40种MTEA、150余个包含实际应用场景的MTO问题实例，以及10余种性能评价指标。此外，为便于MTEA与传统进化算法的对比分析，我们改造了40余种经典单任务进化算法以适配MTO问题求解。MToP配备用户友好的图形界面，支持结果分析、数据导出与流程图可视化。更关键的是，MToP在设计上注重可扩展性，允许用户开发新算法并应对新兴问题领域。MToP的源代码可通过 https://github.com/intLyc/MTO-Platform 获取。