Evolutionary multitasking (EMT) has been attracting much attention over the past years. It aims to handle multiple optimization tasks simultaneously within limited computing resources assisted by inter-task knowledge transfer techniques. Numerous multitask evolutionary algorithms (MTEAs) for solving multitask optimization (MTO) problems have been proposed in the EMT field, but there lacks a comprehensive software platform to help researchers evaluate MTEA performance on benchmark MTO problems as well as explore real-world applications. To address this issue, we introduce the first open-source optimization platform, named MTO-Platform (MToP), for EMT. It incorporates more than 30 MTEAs, more than 150 MTO problem cases with real-world applications, and more than 10 performance metrics. Moreover, for comparing MTEAs with traditional evolutionary algorithms, we modified more than 30 popular single-task evolutionary algorithms to be able to solve MTO problems in MToP. MToP is a user-friendly tool with a graphical user interface that makes it easy to analyze results, export data, and plot schematics. More importantly, MToP is extensible, allowing users to develop new algorithms and define new problems. The source code of MToP is available at https://github.com/intLyc/MTO-Platform.

翻译：近年来，演化多任务优化备受关注。它旨在借助跨任务知识迁移技术，在有限计算资源下同时处理多个优化任务。尽管演化多任务领域已涌现出大量用于求解多任务优化问题的多任务演化算法，但当前仍缺乏一个综合性软件平台来帮助研究人员评估MTeA在基准MTO问题上的性能以及探索实际应用场景。为解决这一问题，我们首次提出了面向演化多任务的开源优化平台MTO-Platform（MToP）。该平台集成了超过30种MTeA、150余个包含实际应用的MTO问题案例以及10余种性能评价指标。此外，为便于MTeA与传统演化算法进行对比，我们在MToP中改造了30余种流行的单任务演化算法，使其能够求解MTO问题。MToP具备图形用户界面，是一款用户友好型工具，可便捷地分析结果、导出数据并绘制示意图。更重要的是，MToP具有可扩展性，允许用户开发新算法并定义新问题。MToP的源代码可从https://github.com/intLyc/MTO-Platform获取。