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MToP: A MATLAB Optimization Platform for Evolutionary Multitasking (2312.08134v3)

Published 13 Dec 2023 in cs.NE

Abstract: 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.

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Summary

  • The paper introduces MToP, a comprehensive MATLAB platform integrating over 30 multitask evolutionary algorithms and 150 optimization problems.
  • It offers both a user-friendly GUI and a command-line interface to visualize optimizer landscapes, convergence behavior, and Pareto fronts.
  • The paper highlights the platform's extensibility through a clear API, fostering community contributions and continuous improvements in evolutionary computation.

Overview of MToP

The field of Evolutionary Multitasking (EMT) which focuses on solving multiple optimization tasks concurrently has gained considerable prominence. Despite the development of various multitask evolutionary algorithms (MTEAs), there has been a lack of an integrated software platform for their evaluation. This gap has been bridged by introducing MToP (MATLAB Optimization Platform), a comprehensive open-source tool designed for evolutionary multitasking.

Key Features and Benefits

MToP stands out with its extensive library, combining over 30 MTEAs and traditional Evolutionary Algorithms (EAs) that have been adapted for multitask purposes. It also features over 150 problem cases inclusive of real-world applications. To ensure a broad utility, MToP comes with more than ten performance metrics, allowing for nuanced analysis of MTEA effectiveness.

Furthermore, the platform is accessible via a user-friendly graphical interface, facilitating easy interaction, data export, and analysis. MToP is architected to not only run analyses but also to visualize results, including graphical displays of optimizer landscapes, feasible regions for tasks with constraints, convergence behavior, and Pareto front figures for multi-objective problems.

Extensibility and Open-Source Contributions

One of the core strengths of MToP is its extensibility. The platform encourages the addition of new algorithms, problems, and performance metrics by following clear coding patterns and making use of its comprehensive API. Being open-source, it supports continuous updates and community-driven enhancements, making the platform ever-evolving.

MToP in Use

For conducting experiments with MToP, users can rely on its GUI for setting up and running tests, comparing algorithm performance, and statistical analysis. For those who prefer command-line tools, MToP also offers a straightforward method for executing and analyzing optimization tasks without the GUI. The ability to handle complex, real-world EMT scenarios, and the inclusion of competitive MTO problems, assures MToP's relevance in ongoing optimization research.

Conclusion

The introduction of MToP represents a significant advancement in the arena of EMT. It is a versatile, comprehensive platform that enables researchers and practitioners to test, develop, and analyze evolutionary multitasking algorithms. Its open-source nature and community involvement promise continuous improvement and a growing repository of resources for those specializing in evolutionary computation.

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  1. GitHub - intLyc/MTO-Platform: Multitask Optimization Platform (MToP) (157 stars)