Constrained Monotone k -Submodular Function Maximization Using Multiobjective Evolutionary Algorithms With Theoretical Guarantee

The problem of maximizing monotone <inline-formula> <tex-math notation="LaTeX">{k} </tex-math></inline-formula>-submodular functions under a size constraint arises in many applications, and it is NP-hard. In this paper, we propose a new approach which employs a mult...

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Veröffentlicht in:IEEE transactions on evolutionary computation Jg. 22; H. 4; S. 595 - 608
Hauptverfasser: Qian, Chao, Shi, Jing-Cheng, Tang, Ke, Zhou, Zhi-Hua
Format: Journal Article
Sprache:Englisch
Veröffentlicht: IEEE 01.08.2018
Schlagworte:
Constrained optimization
Electronic mail
Evolutionary computation
experimental studies
Greedy algorithms
Integrated circuit modeling
Method of moments
multiobjective evolutionary algorithms (MOEAs)
Optimization
submodular optimization
theoretical analysis
ISSN:1089-778X, 1941-0026
Online-Zugang:Volltext
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Zusammenfassung:The problem of maximizing monotone <inline-formula> <tex-math notation="LaTeX">{k} </tex-math></inline-formula>-submodular functions under a size constraint arises in many applications, and it is NP-hard. In this paper, we propose a new approach which employs a multiobjective evolutionary algorithm to maximize the given objective and minimize the size simultaneously. For general cases, we prove that the proposed method can obtain the asymptotically tight approximation guarantee, which was also achieved by the greedy algorithm. Moreover, we further give instances where the proposed approach performs better than the greedy algorithm on applications of influence maximization, information coverage maximization, and sensor placement. Experimental results on real-world data sets exhibit the superior performance of the proposed approach.
ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2017.2749263