Kuhn-Munkres parallel genetic algorithm for the set cover problem and its application to large-scale wireless sensor networks

Zhang, X.-Y., Gong, Y.-j., Zhan, Z., Chen, W.-N., Li, Y. and Zhang, J. (2016) Kuhn-Munkres parallel genetic algorithm for the set cover problem and its application to large-scale wireless sensor networks. IEEE Transactions on Evolutionary Computation, 20(5), pp. 695-710. (doi: 10.1109/TEVC.2015.2511142)

Abstract

Operating mode scheduling is crucial for the lifetime of wireless sensor networks. However, the growing scale of networks has made such a scheduling problem more and more challenging, as existing set cover and evolutionary algorithms become unable to provide satisfactory efficiency due to the curse of dimensionality. In this paper, a Kuhn-Munkres parallel genetic algorithm is developed to solve the set cover problem and is applied to lifetime maximization of large-scale wireless sensor networks. The proposed algorithm schedules the sensors into a number of disjoint complete cover sets and activates them in batch for energy conservation. It uses a divide-and-conquer strategy of dimensionality reduction, and the polynomial Kuhn-Munkres algorithm are hence adopted to splice the feasible solutions obtained in each subarea to enhance the search efficiency substantially. To further improve global efficiency, a redundant-trend sensor schedule strategy is developed. Additionally, we meliorate the evaluation function through penalizing incomplete cover sets, which speeds up convergence. Eight types of experiments are conducted on a distributed platform to test and inform the effectiveness of the proposed algorithm. The results show that it offers promising performance in terms of the convergence rate, solution quality, and success rate.

• Item Type: Articles
• Additional Information: This work was partially supported by the National Natural Science Foundation of China (NSFC) Key Project No. 61332002 and by the NSFC Youth Projects No. 61502542 and No. 61300044.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Li, Professor Yun
• Authors: Zhang, X.-Y., Gong, Y.-j., Zhan, Z., Chen, W.-N., Li, Y., and Zhang, J.
• College/School: College of Science and Engineering > School of Engineering > Systems Power and Energy
• Journal Name: IEEE Transactions on Evolutionary Computation
• Publisher: IEEE
• ISSN: 1089-778X
• Published Online: 22 December 2015
• Copyright Holders: Copyright © 2015 IEEE
• First Published: First published in IEEE Transactions on Evolutionary Computation 20(5): 695-710
• Publisher Policy: Reproduced in accordance with the publisher copyright policy