🤖 AI Summary
The multi-task optimization (MTO) community lacks a unified, open-source evaluation platform, hindering reproducibility, fair benchmarking, and practical validation of evolutionary multitasking (EMT) algorithms.
Method: This paper introduces EMT-Platform—the first open-source MATLAB platform dedicated to EMT research—featuring a modular architecture, plugin-based algorithm interfaces, a graphical user interface, and built-in knowledge transfer mechanisms. It integrates over 50 multitasking algorithms (including systematically adapted single-task baselines), 200+ standardized benchmark problem instances, and 20+ performance metrics.
Contribution/Results: EMT-Platform enables standardized algorithm development, rigorous cross-algorithm evaluation, and intuitive result visualization. It significantly enhances reproducibility, accelerates empirical research, and supports real-world application validation across diverse domains. Widely adopted by the EMT research community, it serves as a foundational infrastructure for advancing both theoretical and applied multitasking optimization.
📝 Abstract
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.
