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 20 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）近年来已成为进化计算领域的热门研究方向。其核心目标是在有限计算资源下，通过任务间知识迁移技术，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），但学术界仍缺乏一个全面的软件平台来帮助研究者在基准MTO问题上评估MTEA性能，并探索其实际应用。为填补这一空白，我们推出了首个面向EMT的开源优化平台——MTO-Platform（简称MToP）。该平台整合了40余种MTEA算法、包含实际应用场景的150多个MTO问题案例，以及20余种性能评价指标。此外，为促进MTEA与传统进化算法的对比分析，我们适配了40多种经典单任务进化算法以处理MTO问题。MToP具备友好的图形用户界面，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。