Abstract
The genetic algorithm is widely used in optimization problems, in which, a population of candidate solutions is mutated and altered toward better solutions. Usually, genetic algorithm works in optimization problem with a fitness function which is used to evaluate the feasibility and quality of a solution. However, sometimes, it is hard to define the fitness function when there are several optimization objectives, especially only one solution can be selected from a population. In this paper, we modified genetic algorithms with a novel-sorting process to solve the above problem. Two algorithms, the classic genetic algorithm and newly proposed recently M-Genetic algorithm, are simulated and altered by embedding the novel-sorting process. Besides, both the algorithms and their alteration versions are applied into wireless sensor network for locating Relay nodes. The sensor node loss and package loss number are reduced in genetic algorithms with our sorting process compared to the original ones.
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References
Moghadam RA, Keshmirpour M (2011) Hybrid ARIMA and neural network model for measurement estimation in energy-efficient wireless sensor networks. In: International conference on informatics engineering and information science. Springer, Berlin, Heidelberg, pp 35–48
Almajidi AM, Pawar VP, Alammari A (2019) K-means-based method for clustering and validating wireless sensor network. In: International conference on innovative computing and communications. Springer, Singapore, pp 251–258
Leu JS, Chiang TH, Yu MC et al (2015) Energy efficient clustering scheme for prolonging the lifetime of wireless sensor network with isolated nodes. IEEE Commun Lett 19(2):259–262
Vallimayil A, Raghunath KMK, Dhulipala VRS et al (2011) Role of relay node in wireless sensor network: a survey. In: 2011 3rd international conference on electronics computer technology (ICECT). IEEE, vol 5, pp 160–167
Gupta SK, Kuila P, Jana PK (2016) Genetic algorithm for k-connected relay node placement in wireless sensor networks. In: Proceedings of the second international conference on computer and communication technologies. Springer, New Delhi, pp 721–729
Azharuddin M, Jana P K (2015) A GA-based approach for fault tolerant relay node placement in wireless sensor networks. In: 2015 third international conference on computer, communication, control and information technology (C3IT). IEEE, pp 1–6
Esmin AAA, Coelho RA, Matwin S (2015) A review on particle swarm optimization algorithm and its variants to clustering high-dimensional data. Artifi Intell Rev 44(1):23–45
Sun Y, Dong W, Chen Y (2017) An improved routing algorithm based on ant colony optimization in wireless sensor networks. IEEE Commun Lett 21(6):1317–1320
Du KL, Swamy MNS (2016) Particle swarm optimization. In: Search and optimization by metaheuristics. Birkhäuser, Cham, pp 153–173
Chandirasekaran D, Jayabarathi T (2017) Cat swarm algorithm in wireless sensor networks for optimized cluster head selection: a real time approach. Cluster Comput 1–11
Kumar S, Nayyar A, Kumari R (2019) Arrhenius artificial Bee Colony Algorithm. In: International conference on innovative computing and communications. Springer, Singapore, pp 187–195
Luo J, Li X, Chen MR et al (2015) A novel hybrid shuffled frog leaping algorithm for vehicle routing problem with time windows. Inf Sci 316:266–292
Elhoseny M, Yuan X, Yu Z et al (2015) Balancing energy consumption in heterogeneous wireless sensor networks using genetic algorithm. IEEE Commun Lett 19(12):2194–2197
Gupta SK, Kuila P, Jana PK (2016) Genetic algorithm approach for k-coverage and m-connected node placement in target based wireless sensor networks. Comput Electr Eng 56:544–556
Arasu A, Novak J, Tomkins A et al (2002) PageRank computation and the structure of the web: experiments and algorithms. In: Proceedings of the eleventh international World Wide Web conference. Poster Track, pp 107–117
Langville AN, Meyer CD (2011) Google’s PageRank and beyond: the science of search engine rankings. Princeton University Press
George J, Sharma RM (2016) Relay node placement in wireless sensor networks using modified genetic algorithm. In: 2016 2nd international conference on applied and theoretical computing and communication technology (iCATccT). IEEE, pp 551–556
Marta M, Cardei M (2009) Improved sensor network lifetime with multiple mobile sinks. Pervasive Mob Comput 5(5):542–555
Acknowledgements
This work was supported by the ESF in “Science without borders” project, \(reg. nr.CZ.02.2.69/0.0/0.0/16\_027/0008463\) within the Operational Programme Research, Development and Education.
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Snášel, V., Kong, LP. (2020). Modified Genetic Algorithm with Sorting Process for Wireless Sensor Network. In: Khanna, A., Gupta, D., Bhattacharyya, S., Snasel, V., Platos, J., Hassanien, A. (eds) International Conference on Innovative Computing and Communications. Advances in Intelligent Systems and Computing, vol 1059. Springer, Singapore. https://doi.org/10.1007/978-981-15-0324-5_33
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DOI: https://doi.org/10.1007/978-981-15-0324-5_33
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