MToP: A MATLAB Optimization Platform for Evolutionary Multitasking

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.