Deployment in wireless sensor networks by parallel and cooperative parallel artificial bee colony algorithms
Deployment in wireless sensor networks by parallel and cooperative parallel artificial bee colony algorithms
Increasing number of cores in a processor chip and decreasing cost of dis-tributed memory based system setup have led to emerge of a new work themein which the main concern focused on the parallelization of the well-knownalgorithmic approaches for utilizing the computational power of the currentarchitectures. In this study, the performances of the conventional paralleland cooperative model based parallel Artificial Bee Colony (ABC) algorithmson the deployment problem related to the wireless sensor networks were in-vestigated. The results obtained from the experimental studies showed thatparallelized ABC algorithm with the cooperative model is capable of findingsimilar or better coverage ratios with the increased convergence speeds thanits serial counterpart and parallelized implementation in which the emigrant ischosen as the best food source in the current subcolony.
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- Akyildiz, I.F., Su, W., Sankarasubramaniam,
Y., Cayirci, E. (2002). Wireless sensor net-
works: a survey. Computer Networks, 38, 393-
422.
- Chakrabarty, K., Iyengar, S.S., Qi, H., Cho,
E. (2002). Grid coverage for surveillance and
target location in distributed sensor networks. IEEE Transactions on Computers, 51, 1448-
1453.
- Bhondekar, A.P., Vig, R., Singla, M.L., C.
Ghanshyam, Kapur, P. (2009). Genetic algo-
rithm based node placement methodology for
wireless sensor networks. Proceedings of the
International Multiconference on Engineers
and Computer Scientists, 1, 18-20.
- Okay, F.Y., Ozdemir, S. (2015). Kablosuz
algılayıcı aglarda kapsama alanının cok amalı
evrimsel algoritmalar ile artırılması. Journal
of the Faculty of Engineering & Architecture
of Gazi University, 30, 143-153.
- Li, Z., Lei, L. (2009). Sensor node deploy-
ment in wireless sensor networks based on
improved particle swarm optimization. Ap-
plied Superconductivity and Electromagnetic
Devices, 215-217.
- Udgata, S.K., Sabat, S.L., Mini, S. (2009).
Sensor deployment in irregular terrain using
artificial bee colony algorithm. Nature & Bi-
ologically Inspired Computing, 1309-1314 .
- Ozturk, C., Karaboga, D., Gorkemli, B.
(2011). Probabilistic dynamic deployment of
wireless sensor networks by artificial bee
colony algorithm. Sensors, 11, 6056-6065 .
- Ozturk, C., Karaboga, D., Gorkemli, B.
(2012). Artificial bee colony algorithm for
dynamic deployment of wireless sensor net-
works. Turkish Journal of Electrical Engi-
neering & Computer Sciences, 20, 255-262.
- Yu, X., Zhang, J., Fan, J., Zhang, T. (2013).
A faster convergence artificial bee colony al-
gorithm in sensor deployment for wireless sen-
sor networks. International Journal of Dis-
tributed Sensor Networks, 9, 1-15.
- Yadav, R.K., Gupdaa, D., Lobiyal, D.K.
(2017). Dynamic positionin of mobile sen-
sors using modified artificial bee colony al-
gorithm in wireless sensor networks. Interna-
tional Journal of Control Theory and Appli-
cations, 10, 167-176.
- Karaboga, D., Akay, B. (2009). A suvery: al-
gorithms simulating bee swarm intelligence.
Artificial Intelligence Reviews, 31, 233-253.
- Bansal, J.C., Sharma, H., Jadon, S.S. (2013).
Artificial bee colony algorithm: a survey. In-
ternational Journal of Advanced Intelligence,
5, 123-159.
- Bolaji, A.L., Khader, A.T., Al-betar, M.A.,
Awadallah, M.A. (2013). Artificial bee colony
algorithm, its variants and applications: a
survey. Journal of Theorical and Applied In-
formation Technology, 47, 434-459.
- Karaboga, D., Akay, B. (2007). A powerful
and efficient algorithm for numerical function
optimization: artificial bee colony algorithm.
Journal of Global Optimization, 39, 459-471.
- Karaboga, D., Akay, B. (2008). On the per-
formance of artificial bee colony algorithm.
Applied Soft Computing, 8, 687-697.
- Akay, B., Karaboga, D. (2012). Artificial bee
colony algorithm for large-scale problems and
engineering design optimization. Journal of
Intelligent Manufacturing, 23, 1001-1014.
- Celik, M., Koylu F., Karaboga, D. (2015).
CoABCMiner: an algorithm for cooperative
rule classification system based on artificial
bee colony algorithm. International Journal
of Artificial Intelligence Tools, 24, 1-50.
- Karaboga, D., Aslan, S. (2016). Best sup-
ported emigrant creation for parallel imple-
mentation of artificial bee colony algorithm.
IU-Journal of Electrical & Electronics Engi-
neering, 16, 2055-2064.
- Badem, H., Basturk, A., Caliskan, A., Yuk-
sel, M.E. (2017). A new efficient train-
ing strategy for deep neural networks by
hybridization of artificial bee colony and
limited-memory BFGS optimization algo-
rithms. Neurocomputing, 266, 506-526.
- Badem, H., Basturk, A., Caliskan, A., Yuk-
sel, M.E. (2018). A new hybrid optimization
method combining artificial bee colony and
limited-memory BFGS algorithms for efficient
numerical optimization. Applied Soft Com-
puting, 266, 506-526 .
- Akay, B., Karaboga, D. (2017). Artificial bee
colony algorithm variants on constrained op-
timization. An Internation Journal of Opti-
mization and Control: Theories & Applica-
tions, 7, 98-111.
- Ozturk, C., Aslan, S. (2016). A new artificial
bee colony algorithm to solve the multiple se-
quence alignment problem. Internation Jour-
nal of Data Mining and Bioinformatics, 14,
332-352.
- Karaboga, D., Aslan, S. (2016). A discrete
artificial bee colony algorithm for detecting
transcription factor binding sites in DNA se-
quences. Genetics and Molecular Research,
15, 1-11.
- Narasimhan, H. (2009). Parallel artificial bee
colony algorithm. Nature & Biologically In-
spired Computing, 306-311.
- Banharnsakun, A., Tiranee, A., Boon-
charoen, S. (2010). Artificial bee colony al-
gorithm on distributed environment. Nature
& Biologically Inspired Computing, 13-18 .
- Karaboga, D., Aslan, S. (2016). A new em-
igrant creation strategy based on local best
sources for parallel artificial bee colony algo-
rithm. In 24th Signal Processing and Commu-
nication Application Conference, 901-904.