oalogo2  

AUTHOR(S):

Ivana Strumberger, Marko Sarac, Dusan Markovic, Nebojsa Bacanin

 

TITLE

Moth Search Algorithm for Drone Placement Problem

pdf PDF

ABSTRACT

This paper presents implementation of the moth search algorithm adjusted for solving static drone location problem. The optimal location of drones is one of the most important issues in this domain, and it belongs to the group of NP-hard optimization. The objective of the model applied in this paper is to establish monitoring all targets with the least possible number of drones. For testing purposes, we used problem instance with 30 uniformly distributed targets in the network domain. According to the results of simulations, where moth search algorithm established full coverage of targets, this approach shows potential in dealing with this kind of problem.

KEYWORDS

moth search algorithm, metaheuristics, NP hardness, swarm intelligence, optimization

REFERENCES

[1] H. Chen, X. min Wang, and Y. Li, “A survey of autonomous control for uav,” in Proceedings of the 09 International Conference on A Artificial Intelligence and Computational Intelligence (AICI ´ 09), pp. 267–271, IEEE, November 2009.

[2] D. Zorbas, L. D. P. Pugliese, T. Razafindralambo, and F. Guerriero, “Optimal drone placement and cost-efficient target coverage,” Journal of Network and Computer Applications, vol. 75, pp. 16–31, November 2016.

[3] M. Younis and K. Akkaya, “Strategies and techniques for node placement in wireless sensor networks: A survey,” Ad Hoc Networks, vol. 6, pp. 621–655, June 2008.

[4] D. Karaboga, “An idea based on honey bee swarm for numerical optimization,” Technical Report - TR06, pp. 1–10, 2005.

[5] N. Bacanin, M. Tuba, and I. Brajevic, “Performance of object-oriented software system for improved artificial bee colony optimization,” International Journal of Mathematics and Computers in Simulation, vol. 5, no. 2, pp. 154–162, 2011.

[6] N. Bacanin, M. Tuba, and I. Strumberger, “RFID network planning by ABC algorithm hybridized with heuristic for initial number and locations of readers,” in 2015 17th UKSim-AMSS International Conference on Modelling and Simulation (UKSim), pp. 39–44, March 2015.

[7] X.-S. Yang, “Firefly algorithms for multimodal optimization,” Stochastic Algorithms: Foundations and Applications, LNCS, vol. 5792, pp. 169–178, 2009.

[8] E. Tuba, M. Tuba, and M. Beko, “Mobile wireless sensor networks coverage maximization by firefly algorithm,” in 27th International Conference Radioelektronika, pp. 1–5, IEEE, 2017.

[9] E. Tuba, M. Tuba, and M. Beko, “Two stage wireless sensor node localization using firefly algorithm,” in Smart Trends in Systems, Security and Sustainability, LNNS, vol. 18, pp. 113–120, Springer, 2018.

[10] M. Tuba and N. Bacanin, “JPEG quantization tables selection by the firefly algorithm,” in International Conference on Multimedia Computing and Systems (ICMCS), pp. 153–158, IEEE, 2014.

[11] X.-S. Yang and S. Deb, “Cuckoo search via levy flights,” in Proceedings of World Congress on Nature & Biologically Inspired Computing (NaBIC 2009), pp. 210–214, 2009.

[12] I. Brajevic and M. Tuba, “Cuckoo search and firefly algorithm applied to multilevel image thresholding,” in Cuckoo Search and Firefly Algorithm: Theory and Applications (X.-S. Yang, ed.), vol. 516 of Studies in Computational Intelligence, pp. 115–139, Springer International Publishing, 2014.

[13] N. Bacanin, “Implementation and performance of an object-oriented software system for cuckoo search algorithm,” International Journal of Mathematics and Computers in Simulation, vol. 6, pp. 185–193, December 2010.

[14] Y. Tan and Y. Zhu, “Fireworks algorithm for optimization,” Advances in Swarm Intelligence, LNCS, vol. 6145, pp. 355–364, June 2010.

[15] E. Tuba, M. Tuba, and E. Dolicanin, “Adjusted fireworks algorithm applied to retinal image registration,” Studies in Informatics and Control, vol. 26, no. 1, pp. 33–42, 2017.

[16] N. Bacanin and M. Tuba, “Fireworks algorithm applied to constrained portfolio optimization problem,” in Proceedings of the 2015 IEEE Congress on Evolutionary Computation (CEC 2015), May 2015.

[17] M. Tuba, N. Bacanin, and A. Alihodzic, “Multilevel image thresholding by fireworks algorithm,” in 2015 25th International Conference Radioelektronika (RADIOELEKTRONIKA), pp. 326–330, April 2015.

[18] E. Tuba, M. Tuba, and M. Beko, “Node localization in ad hoc wireless sensor networks using fireworks algorithm,” in Proceedings of the 5th International Conference on Multimedia Computing and Systems (ICMCS), pp. 223–229, September 2016.

[19] E. Tuba, M. Tuba, and D. Simian, “Wireless sensor network coverage problem using modified fireworks algorithm,” in International Wireless Communications and Mobile Computing Conference (IWCMC), pp. 696–701, IEEE, 2016.

[20] X.-S. Yang, “A new metaheuristic bat-inspired algorithm,” Studies in Computational Intelligence, vol. 284, pp. 65–74, November 2010.

[21] E. Tuba, M. Tuba, and D. Simian, “Adjusted bat algorithm for tuning of support vector machine parameters,” in IEEE Congress on Evolutionary Computation (CEC), pp. 2225–2232, IEEE, 2016.

[22] M. Tuba and N. Bacanin, “Hybridized bat algorithm for multi-objective radio frequency identification (RFID) network planning,” in Proceedings of the 2015 IEEE Congress on Evolutionary Computation (CEC 2015), May 2015.

[23] E. Tuba, M. Tuba, and D. Simian, “Handwritten digit recognition by support vector machine optimized by bat algorithm,” Proceeding of the WSCG 2016, Computer Science Research Notes, pp. 369–376, 2016.

[24] E. Dolicanin, I. Fetahovic, E. Tuba, R. Capor- Hrosik, and M. Tuba, “Unmanned combat aerial vehicle path planning by brain storm optimization algorithm,” Studies in Informatics and Control, vol. 27, no. 1, pp. 15–24, 2018.

[25] E. Tuba, R. Capor-Hrosik, A. Alihodzic, and M. Tuba, “Drone placement for optimal coverage by brain storm optimization algorithm,” in International Conference on Health Information Science, Advances in Intelligent Systems and Computing, vol. 734, pp. 167–176, Springer, 2017.

[26] E. Tuba, E. Dolicanin, and M. Tuba, “Chaotic brain storm optimization algorithm,” in Intelligent Data Engineering and Automated Learning, LNCS, vol. 10585, (Cham), pp. 551–559, Springer International Publishing, 2017.

[27] G.-G. Wang, “Moth search algorithm: a bioinspired metaheuristic algorithm for global optimization problems,” Memetic Computing, Sep 2016.

[28] D. Zorbas, T. Razafindralambo, D. P. P. Luigi, and F. Guerriero, “Energy ecient mobile target tracking using ying drones,” in Proceedings of the 4th International Conference on Ambient Systems, Networks and Technologies (ANT 2013), Procedia Computer Science, vol. 19, pp. 80–87, Springer, 2013.

[29] P. S. Callahan, “Moth and candle: The candle flame as a sexual mimic of the coded infrared wavelengths from a moth sex scent (pheromone),” Applied Optics, vol. 16, no. 12, pp. 3089–3097, 1977.

[30] A. Reynolds, D. Reynolds, A. Smith, G. Svensson, and C. Lfstedt, “Appetitive flight patterns of male agrotis segetum moths over landscape scales,” Journal of Theoretical Biology, vol. 245, no. 1, pp. 141 – 149, 2007.

[31] A. H. Gandomi and A. H. Alavi, “Krill herd: A new bio-inspired optimization algorithm,” Commun Nonlinear Sci Numer Simulat, vol. 17, pp. 4831–4845, 2012.

Cite this paper

Ivana Strumberger, Marko Sarac, Dusan Markovic, Nebojsa Bacanin. (2018) Moth Search Algorithm for Drone Placement Problem. International Journal of Computers, 3, 75-80

 

cc.png
Copyright © 2018 Author(s) retain the copyright of this article.
This article is published under the terms of the Creative Commons Attribution License 4.0