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 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 50 MTEAs, more than 200 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 50 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：进化多任务优化在过去十年中已成为进化计算领域的热门研究方向。该技术旨在利用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问题提出的多任务进化算法，但学界仍缺乏一个能够帮助研究者在基准多任务优化问题上评估算法性能、并探索实际应用场景的综合性软件平台。为填补这一空白，我们推出了首个面向进化多任务优化的开源平台——MTO-Platform（简称MToP）。该平台整合了50余种多任务进化算法、包含实际应用场景的200多个多任务优化问题案例以及{超过20项性能评价指标}。此外，为促进多任务进化算法与传统进化算法的对比分析，我们还改造了50多种经典单任务进化算法以处理多任务优化问题。MToP具备友好的图形用户界面，支持结果分析、数据导出及示意图可视化。更重要的是，该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。