Gas Source Localization using Grey Wolf Optimizer

Authors

  • S.M. Mamduh Centre of Excellence for Advanced Sensor Technology (CEASTech), Universiti Malaysia Perlis (UniMAP), Arau, Perlis 02600. Pusat Pengajian Kejuruteraan Mikroelektronik, Universiti Malaysia Perlis (UniMAP), Arau, Perlis 02600.
  • K. Kamarudin Centre of Excellence for Advanced Sensor Technology (CEASTech), Universiti Malaysia Perlis (UniMAP), Arau, Perlis 02600. Pusat Pengajian Kejuruteraan Mekatronik, Universiti Malaysia Perlis (UniMAP), Arau, Perlis 02600.
  • A.Y.M. Shakaff Centre of Excellence for Advanced Sensor Technology (CEASTech), Universiti Malaysia Perlis (UniMAP), Arau, Perlis 02600. Pusat Pengajian Kejuruteraan Mekatronik, Universiti Malaysia Perlis (UniMAP), Arau, Perlis 02600.
  • A. Zakaria Centre of Excellence for Advanced Sensor Technology (CEASTech), Universiti Malaysia Perlis (UniMAP), Arau, Perlis 02600. Pusat Pengajian Kejuruteraan Mekatronik, Universiti Malaysia Perlis (UniMAP), Arau, Perlis 02600.
  • R. Visvanathan Centre of Excellence for Advanced Sensor Technology (CEASTech), Universiti Malaysia Perlis (UniMAP), Arau, Perlis 02600. Pusat Pengajian Kejuruteraan Mekatronik, Universiti Malaysia Perlis (UniMAP), Arau, Perlis 02600.
  • A.S.A. Yeon Centre of Excellence for Advanced Sensor Technology (CEASTech), Universiti Malaysia Perlis (UniMAP), Arau, Perlis 02600. Pusat Pengajian Kejuruteraan Mekatronik, Universiti Malaysia Perlis (UniMAP), Arau, Perlis 02600.
  • L.M. Kamarudin Centre of Excellence for Advanced Sensor Technology (CEASTech), Universiti Malaysia Perlis (UniMAP), Arau, Perlis 02600. Pusat Pengajian Kejuruteraan Komputer dan Perhubungan, Universiti Malaysia Perlis (UniMAP), Arau, Perlis 02600.
  • A.S.A. Nasir Fakulti Teknologi Kejuruteraan, Universiti Malaysia Perlis (UniMAP), Padang Besar, Perlis 02100.

Keywords:

Gas Source Localization, Grey Wolf Optimizer, Mobile Olfaction, Swarm Intelligence,

Abstract

Gas source localization is an ability which has yet to be successfully implemented in synthetic systems although it is widely exhibited by various organisms. Although single robot implementation has been explored, it is still prone to single point failures and is limited in sporadic gas dispersion conditions. Swarm intelligence based algorithms such as Particle Swarm Optimization and Ant Colony Optimization has shown the feasibility and advantage of using multi-robot strategy for gas source localization. This paper explores Grey Wolf Optimizer (GWO) as an alternative algorithm for gas source localization. It was found that, although some GWO search behavior is favorable for gas source localization, the algorithm may fail when used with low numbers of robots. The algorithm was able to localize the peak gas concentration in approximately 30 minutes. The best success rate is found to be 72% with 7 searcher robots.

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Published

2018-05-29

How to Cite

Mamduh, S., Kamarudin, K., Shakaff, A., Zakaria, A., Visvanathan, R., Yeon, A., Kamarudin, L., & Nasir, A. (2018). Gas Source Localization using Grey Wolf Optimizer. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 10(1-13), 95–98. Retrieved from https://jtec.utem.edu.my/jtec/article/view/4130

Issue

Vol. 10 No. 1-13: Exploring Man-Machines Compliance in Signal Processing and Artificial Intelligence

Section

Articles

License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)

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