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Description: This package includes the JAVA code of the MOMS algorithm [1] for maximizing monotone k-submodular functions under a size constraint. It uses a simple multi-objective evolutionary algorithm combined with randomized local search to solve the bi-objective reformulation of the original problem: maximizing the monotone k-submodular objective function and minimizing the size. A Readme file and an example file are included in the package. In the '', you will find an example of using this code for the application of sensor placement with k kinds of sensors on a real-world data set (


[1] Chao Qian, Jing-Cheng Shi, Ke Tang, and Zhi-Hua Zhou. Constrained Monotone k-Submodular Function Maximization Using Multi-objective Evolutionary Algorithms with Theoretical Guarantee. IEEE Transactions on Evolutionary Computation.

ATTN: This package is free for academic usage. You can run it at your own risk. For other purposes, please contact Prof. Zhi-Hua Zhou (

Requirement: The package was developed with JAVA.

ATTN2: This package was developed by Mr. Jing-Cheng Shi ( For any problem concerning the code, please feel free to contact Mr. Shi.

Download: [code] (57KB)
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