Hybrid Based Selective Genetic Algorithm
Keywords:Meta-heuristic searching optimization, Genetic algorithms, Roulette wheel, Pairwise tournament
Wireless Sensor Network WSN deployment is an active research area. Its goal is to deploy sensors in certain environments efficiently to optimize some evaluation measures. Meta heuristic searching optimization approaches have been proven to be effective in solving WSN deployment problem. They have strong power in exploring the solution space and converging toward the optimal region. One key factor in achieving more exploring power in the meta-heuristic searching is the selection criteria of the elite solutions from one iteration to another. Two common selection criteria are roulette wheel and pairwise tournament. In this article, a hybrid based selection is applied under genetic algorithm for solving the problem of WSND. The hybrid based selection selects between roulette wheel and pairwise tournament in order to maintain good exploration and fast convergence toward the best solutions.
Hashim, H. A., Ayinde, B. O., & Abido, M. A. (2016). Optimal placement of relay nodes in wireless sensor network using artificial bee colony algorithm. Journal of Network and Computer Applications, 64, 239-248.
Jameii, S. M., Faez, K., & Dehghan, M. (2016). AMOF: Adaptive multi-objective optimization framework for coverage and topology control in heterogeneous wireless sensor networks. Telecommunication Systems, 61(3), 515-530.
Keskin, M. E., Altınel, I. K., Aras, N., & Ersoy, C. (2014). Wireless sensor network lifetime maximization by optimal sensor deployment, activity scheduling, data routing and sink mobility. Ad Hoc Networks, 17, 18-36.
Khalesian, M., & Delavar, M. R. (2016). Wireless sensors deployment optimization using a constrained Pareto-based multi-objective evolutionary approach. Engineering Applications of Artificial Intelligence, 53, 126-139.
Sengupta, S., Das, S., Nasir, M. D., & Panigrahi, B. K. (2013). Multi-objective node deployment in WSNs: In search of an optimal trade-off among coverage, lifetime, energy consumption, and connectivity. Engineering Applications of Artificial Intelligence, 26(1), 405-416.
Xu, X., & Sahni, S. (2007). Approximation algorithms for sensor deployment. IEEE Transactions on Computers, 56(12), 1681-1695.
Copyright (c) 2022 Ahmed Kawther Hussein
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.