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How good are distributed allocation algorithms for solving urban search and rescue problems? A comparative study with centralized algorithms

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journal contribution
posted on 2018-11-01, 16:02 authored by Na Geng, Qinggang MengQinggang Meng, Dunwei Gong, Paul Chung
In this paper, a modified centralized algorithm based on particle swarm optimization (MCPSO) is presented to solve the task allocation problem in the search and rescue domain. The reason for this paper is to provide a benchmark against distributed algorithms in search and rescue application area. The hypothesis of this paper is that a centralized algorithm should perform better than distributed algorithms because it has all the available information at hand to solve the problem. Therefore, the centralized approach will provide a benchmark for evaluating how well the distributed algorithms are working and how much improvement can still be gained. Among the distributed algorithms, the consensus-based bundle algorithm (CBBA) is a relatively recent method based on the market auction mechanism, which is receiving considerable attention. Other distributed algorithms, such as PI and PI with softmax, have shown to perform better than CBBA. Therefore, in this paper, the three distributed algorithms mentioned earlier are compared against three centralized algorithms. They are particle swarm optimization, MCPSO, described in this paper, and genetic algorithms. Two experiments were conducted. The first involved comparing all the above-mentioned algorithms, both centralized and distributed, using the same set of application scenarios. It is found that MCPSO always outperforms the other five algorithms in time cost. Due to the high failure rate of CBBA and the other two centralized methods, the second experiment focused on carrying out more tests to compare MCPSO against PI and PI with softmax. All the results are shown and analyzed to determine the performance gaps between the distributed algorithms and the MCPSO.

Funding

This work was supported in part by the National Natural Science Foundation of China under Grant 61703188, Grant 61673404, and Grant 61873105, in part by the National Basic Research Program of China under Grant 2014CB046306-2, in part by the Natural Science Foundation of Jiangsu under Grant BK20160219, and in part by the Natural Science Foundation of Jiangsu Normal University under Grant 16XLR043.

History

School

  • Science

Department

  • Computer Science

Published in

IEEE Transactions on Automation Science and Engineering

Citation

GENG, N. ... et al, 2018. How good are distributed allocation algorithms for solving urban search and rescue problems? A comparative study with centralized algorithms. IEEE Transactions on Automation Science and Engineering, 16 (1), pp.478-485.

Publisher

© Institute of Electrical and Electronics Engineers (IEEE)

Version

  • AM (Accepted Manuscript)

Acceptance date

2018-08-10

Publication date

2018

Notes

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

ISSN

1545-5955

eISSN

1558-3783

Language

  • en