Abstract
In this paper, we present an efficient adaptive artificial bee colony (EA-ABC) algorithm that includes an adaptive mutation mechanism, guaranteed convergence mechanism and optimal tracking mechanism. The EA-ABC algorithm is applied to cooperative spectrum sensing for a cognitive radio field. An efficient sensing scheme can reduce the false alarm probability and enhance the detection probability, which can improve spectrum utilization. Simulations are conducted to compare the performance of the proposed cooperative spectrum sensing method based on the EA-ABC algorithm with that of the cooperative spectrum sensing method using the ABC, particle swarm optimization (PSO), and modified PSO algorithms. The simulation results validate the effectiveness and reliability of the proposed method and demonstrate that EA-ABC-based can achieve higher detection probability than other methods under the same false alarm probability.
References
Alfi A (2011) PSO with adaptive mutation and inertia weight and its application in parameter estimation of dynamic systems. Acta Autom Sinica 37:541–549
Bergh VD (2002) Analysis of particle swarm optimizers [D]. Pretoria
Cabric D, Mishra SM, Brodersen R (2004) Implementation issues in spectrum sensing for cognitive radios. In: Proceeding 38th Asilomar Conference Signals. Systems and Computers, Pacific Grove, USA, vol 1, pp 772–776
Dereli T, Das GS (2011) A hybrid ’bee(s) algorithm’ for solving container loading problems. Appl Soft Comput 11:2854–2862
Haykin S (2005) Cognitive radios: brain-empowered wireless communications. IEEE J Sel Areas Commun 23:201–220
Karaboga D, Basturk B (2008) On the performance of artificial bee colony (ABC) algorithm. Appl Soft Comput 8:687–697
Karaboga D, Basturk B (2009) A comparative study of artificial bee colony algorithm. Appl Math Comput 214:108–132
Karaboga D, Ozturk C (2009) Neural networks training by artificial bee colony algorithm on pattern classification. Neural Netw World 19(3):279–292
Karaboga D, Ozturk C (2011) A novel clustering approach: artificial bee colony (ABC) algorithm. Appl Soft Comput 11:652–657
Karaboga D, Okdem S, Ozturk C (2012) Cluster based wireless sensor network routing using artificial bee colony algorithm. Wirel Netw 18(7):847–860
Karaboga D, Ozturk C, Karaboga N, Gorkemli B (2012) Artificial bee colony programming for symbolic regression. Inform Sci 209:1–15
Karaboga D, Basturk B (2007) Artificial bee colony (ABC) optimization algorithm for solving constrainde optimization problems. Advances in soft computing: Foundation of Fuzzy logic and soft computing, pp 789–798
Krink T, Vesterstrom JS, Riget J (2002) Particle swarm optimization with spatial particle extension. In: Proceedings of IEEE Congress on Evolutionary Computation, Honolulu, Hawaii, pp 1474–1497
Kuo RJ, Yang CY (2011) Simulation optimization using particle swarm optimization algorthm with application to assembly line design. Appl Soft Comput 11:605–613
Li G, Niu P, Xiao X (2012) Development and investigation of efficient artificial bee colony algorithm for numerical function optimization. Appl Soft Comput 12:320–332
Liang Y, Leung KS (2011) Genetic algorithm with adaptive elitist-population strategies for multimodal function optimization. Appl Soft Comput 11:2017–2034
Ma J, Li GY, Juang BH (2009) Signal processing in cognitive radio. Proces IEEE 97:805–823
Mallipeddi R, Suganthan PN, Pan QK, Tasgetiren MF (2011) Differential evolution algorithm with ensemble of parameters and mutation strategies. Appl Soft Comput 11:1679–1696
de Oliveira IMS, Schirru R (2011) Swarm intelligence of artificial bees applied to in-core fuel management optimization. Appl Soft Comput 38:1039–1045
Ozturk C, Karaboga D, Gorkemli B (2011) Probabilistic dynamic deployment of wireless sensor networks by artificial bee colony algorithm. Sensors 11(6):6056–6065
Peh E, Liang YC (2007) Optimization for cooperative sensing in cognitive radio networks. In: Processing IEEE International Wireless Communication and Networking Conference, Hong Kong, pp 27–32
Quan Z, Cui S, Sayed AH (2008) Optimal linear cooperation for spectrum sensing in cognitive radio networks. IEEE J Sel Top Sign Proces 2:28–40
Singh A (2009) An artificial bee colony algorithm for the leaf-constrained minimum spanning tree problem. Appl Soft Comput 9:625–631
Vistotsky E, Kuffner S, Peterson R (2005) On collaborative detection of TV transimissions in support of dynamic spectrum sharing. IEEE Symposium on New Frontiers in Dynamic Spectrum Access Networks, MD, USA, pp 338–345
Yue WJ, Zheng BY (2010) Spectrum sensing algorithms for primary detection based on reliabiliy in cognitive radio systems. Comput Electr Eng 36:469–479
Zhang J-J, Wang Z-S, Yu H (2010) Matching pursuit based on hybrid particle swarm optimization algorithm. J Vib Shock 29:143–147
Zheng S, Lou C, Yang X (2010) Cooperative spectrum sensing using particle swarm optimisation. Electr Lett 46:1525–1526
Acknowledgments
The authors thank the financial support of the Natural Science Foundation of China under Grant No.61172095, 61203104. Nature Science Foundation of Hebei Province under Grant No.F2012203138.
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Communicated by D. Liu.
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Li, X., Lu, L., Liu, L. et al. Cooperative spectrum sensing based on an efficient adaptive artificial bee colony algorithm. Soft Comput 19, 597–607 (2015). https://doi.org/10.1007/s00500-014-1280-2
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DOI: https://doi.org/10.1007/s00500-014-1280-2