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Taguchi yöntemi kullanılarak kablosuz algılayıcı ağların dinamik dağıtım problemi için hızlı yapay arı kolonisi algoritmasının parametre optimizasyonu

Yıl 2022, Cilt: 24 Sayı: 2, 567 - 580, 08.07.2022

Öz

Uygulamanın başarısını etkilerken genellikle zor ve zaman alıcı bir işlem olduğu için iyi bir parametre ayarı bulmak evrimsel hesaplama tabanlı algoritmaların uygulanmasında çok önemli bir konudur. Bu nedenle popüler araştırma alanlarından biridir. Bu çalışmada, kablosuz algılayıcı ağların dinamik dağıtım probleminde hızlı yapay arı kolonisi algoritmasının parametre optimizasyonu için Taguchi yöntemi kullanılarak bir deneysel tasarım çalışması sunulmuştur. Koloni büyüklüğü, limit ve komşuluk yarıçapı tasarım faktörleri olarak dikkate alınmıştır. Kapsanmayan alanın toplam ilgilenilen alana oranı performans karakteristiği olarak kabul edilmiştir. Bilgisayar simülasyonu ile iç ortogonal dizili bir gürbüz deneysel tasarım gerçekleştirilmiş ve optimum parametre ayarı sunulmuştur. Faktörlerin etkileri incelenmiş ve varyans analizi çalışması yapılmıştır. Tahmin edilen ve gerçek sinyal-gürültü oranları arasında bir karşılaştırma yapılmış ve tahminin güvenilirliği küçük bir hata ile doğrulanmıştır.

Kaynakça

  • Akyildiz, I. F., Su, W., Sankarasubramaniam, Y. and Cayirci, E., Wireless sensor networks: a survey, Computer Network, 38, 393-422, (2002).
  • Öztürk, C., Karaboğa, D. and Görkemli, B., Artificial bee colony algorithm for dynamic deployment of wireless sensor networks, Turkish Journal of Electrical Engineering and Computer Sciences, 20, 2, 255-262, (2012).
  • Ozturk, C., Karaboga, D. and Gorkemli, B., Probabilistic dynamic deployment of wireless sensor networks by artificial bee colony algorithm, Sensors, 11, 6, 6056-6065, (2011).
  • Binh, H. T. T., Hanh, N. T., Quan, L. V. and Dey, N., Improved cuckoo search and chaotic flower pollination optimization algorithm for maximizing area coverage in wireless sensor networks, Neural Computing & Applications, 30, 7, 2305-2317, (2018).
  • Tuba, E., Tuba, M. and Beko, M., Mobile wireless sensor networks coverage maximization by firefly algorithm, Proceedings, 2017 International Conference on Radioelektronika, 182-186, Brno, Czech Republic, (2017).
  • Wang, G., Guo, L., Duan, H., Liu, L. and Wang, H., Dynamic deployment of wireless sensor networks by biogeography based optimization algorithm, Journal of Sensor Actuator Networks, 1, 2, 86-96, (2012).
  • He, P. and Jiang, M., Dynamic deployment of wireless sensor networks by an improved artificial bee colony algorithm, Application of Mechanical Materials, 511-512, 862-866, (2014).
  • Özdağ, R. and Karcı, A., Probabilistic dynamic distribution of wireless sensor networks with improved distribution method based on electromagnetism-like algorithm, Measurement, 79, 66-76, (2016).
  • Kukunuru, N., Thella, B. R. and Davuluri, R. L., Sensor deployment using particle swarm optimization, International Journal of Engineering Science and Technology, 2, 10, 5395-5401, (2010).
  • Shan, W. and Chen, X., Improved invasive weed optimization algorithm in sensor deployment for wireless sensor networks, Boletín Técnico, 55, 9, 310-316, (2017).
  • Liu, X. L., Zhang, X. S. and Zhu, Q. X., Enhanced fireworks algorithm for dynamic deployment of wireless sensor networks, Proceedings, 2nd International Conference on Frontiers of Sensors Technologies (ICFST), 161-165, Shenzhen, China, (2017).
  • Wang, X., Wang, S. and Ma, J., An improved co-evolutionary particle swarm optimization for wireless sensor networks with dynamic deployment, Sensors, 7, 3, 354-370, (2007).
  • Wang, L., Wu, W. H., Qi, J. Y. and Jia, Z. P., Wireless sensor network coverage optimization based on whale group algorithm, Computer Science and Information Systems, 15, 3, 569-583, (2018).
  • Farsi, M., Elhosseini, M. A., Badawy, M., Ali, H. A. and Eldin, H. Z., Deployment techniques in wireless sensor networks, coverage and connectivity: a survey, IEEE Access, 7, 28940-28954, (2019).
  • Uppal, R. S. and Kumar, S., Big bang-big crunch algorithm for dynamic deployment of wireless sensor network. International Journal of Electrical and Computer Engineering, 6, 2, 596-601, (2016).
  • Tuba, E., Tuba, M. and Simian, D., Wireless sensor network coverage problem using modified fireworks algorithm, Proceedings, 2016 International Wireless Communications and Mobile Computing Conference (IWCMC), 696-701, Paphos, Cyprus, (2016).
  • Wang, X., Wang, S. and Bi, D., Virtual force-directed particle swarm optimization for dynamic deployment in wireless sensor networks, Lecture Notes in Computer Science: Advanced Intelligent Computing Theories and Applications. With Aspects of Theoretical and Methodological Issues, International Conference on Intelligent Computing 2007, 4681, 292-303, Berlin, Germany: Springer, (2007).
  • Alia, O. M. and Al-Ajouri, A., Maximizing wireless sensor network coverage with minimum cost using harmony search algorithm, IEEE Sensors Journal, 17, 3, 882-896, (2017).
  • Karaboga, D., An idea based on honey bee swarm for numerical optimization, Technical Report-TR06, Erciyes University, Engineering Faculty, ComputerEngineering Department, Kayseri, (2005).
  • Karaboga, D., Artificial bee colony algorithm, Scholarpedia, 5, 3, 6915, (2010). http://www.scholarpedia.org/article/Artificial_bee_colony_algorithm, (25.09.2021).
  • Karaboga, D., Gorkemli, B., Ozturk, C. and Karaboga, N., A comprehensive survey: artificial bee colony (ABC) algorithm and applications, Artificial Intelligence Review, 42, 1, 21-57, (2014).
  • Yu, X., Zhang, J., Fan, J. and Zhang, T., A faster convergence artificial bee colony algorithm in sensor deployment for wireless sensor networks, International Journal of Distributed Sensor Networks, 2013 1-9, (2013).
  • Yadav, R. K., Gupta, D. and Lobiyal, D. K., Dynamic positioning of mobile sensors using modified artificial bee colony algorithm in a wireless sensor networks, International Journal of Control Theory and Applications, 10, 18, 167-176, (2017).
  • Aslan, S., Deployment in wireless sensor networks by parallel and cooperative parallel artificial bee colony algorithms, International Journal of Optimization and Control: Theories & Applications, 9, 1, 1-10, (2019).
  • Aslan, S., A transition control mechanism for artificial bee colony (ABC) algorithm, Computational Intelligence and Neuroscience, 2019, 1-24, (2019).
  • Aslan, S., Aksoy, A. and Gunay, M., Performance of parallel artificial bee colony algorithm on solving probabilistic sensor deployment problem, Proceedings, 2018 International Conference on Artificial Intelligence and Data Processing, 1-21, Malatya, Turkey, (2018).
  • Görkemli, B. and Al-Dulaimi, Z., On the performance of quick artificial bee colony algorithm for dynamic deployment of wireless sensor networks, Turkish Journal of Electrical Engineering and Computer Sciences, 27, 6, 4038-4054, (2019).
  • Karaboga, D., Gorkemli, B., A quick artificial bee colony (qABC) algorithm and its performance on optimization problems, Applied Soft Computing, 23, 227-238, (2014).
  • Mozdgir, A., Mahdavi, I., Badeleh, I. S. and Solimanpur, M., Using the Taguchi method to optimize the differential evolution algorithm parameters for minimizing the workload smoothness index in simple assembly line balancing, Mathematical and Computer Modelling, 57, 1–2, 137-151, (2013).
  • Sun, J. U., A Taguchi approach to parameter setting in a genetic algorithm for general job shop scheduling problem, Industrial Engineering and Management Systems, 6, 2, 119-124, (2007).
  • Yurtkuran, A. and Emel, E., A discrete artificial bee colony algorithm for single machine scheduling problems, International Journal of Production Research, 54, 22, 6860-6878, (2016).
  • Majumdar, A. and Ghosh, D., Genetic algorithm parameter optimization using Taguchi robust design for multi-response optimization of experimental and historical data, International Journal of Computer Applications, 127, 5, 26-32, (2015).
  • Peker M., Şen B. and Kumru P. Y., An efficient solving of the traveling salesman problem: the ant colony system having parameters optimized by the Taguchi method, Turkish Journal of Electrical Engineering and Computer Sciences, 21, 2015-2036, (2013).
  • Gümüş, D., B., Ozcan, E. and Atkin, J., An investigation of tuning a memetic algorithm for cross-domain search, Proceedings, 2016 IEEE Congress on Evolutionary Computation (CEC), 135-142, (2016).

Parameter optimization of quick artificial bee colony algorithm for dynamic deployment problem of wireless sensor networks using Taguchi method

Yıl 2022, Cilt: 24 Sayı: 2, 567 - 580, 08.07.2022

Öz

Finding a good parameter setting is a crucial issue in implementation of evolutionary computation based algorithms since it is generally a difficult and time consuming process while it effects the success of the implementation. So, it is one of the popular research fields. In this paper, an experimental design study is presented for parameter optimization of quick artificial bee colony algorithm in dynamic deployment problem of wireless sensor networks using Taguchi method. Colony size, limit and neighborhood radius are considered as design factors. Ratio of uncovered area to total area of the interest is adopted as the performance characteristic. A robust experimental design with an inner orthogonal array is conducted by computer simulation, and the optimal parameter setting is presented. Effects of the factors are examined, and analysis of variance study is performed. A comparison is hold between the predicted and actual signal to noise ratios and reliability of the prediction is confirmed with a small error.

Kaynakça

  • Akyildiz, I. F., Su, W., Sankarasubramaniam, Y. and Cayirci, E., Wireless sensor networks: a survey, Computer Network, 38, 393-422, (2002).
  • Öztürk, C., Karaboğa, D. and Görkemli, B., Artificial bee colony algorithm for dynamic deployment of wireless sensor networks, Turkish Journal of Electrical Engineering and Computer Sciences, 20, 2, 255-262, (2012).
  • Ozturk, C., Karaboga, D. and Gorkemli, B., Probabilistic dynamic deployment of wireless sensor networks by artificial bee colony algorithm, Sensors, 11, 6, 6056-6065, (2011).
  • Binh, H. T. T., Hanh, N. T., Quan, L. V. and Dey, N., Improved cuckoo search and chaotic flower pollination optimization algorithm for maximizing area coverage in wireless sensor networks, Neural Computing & Applications, 30, 7, 2305-2317, (2018).
  • Tuba, E., Tuba, M. and Beko, M., Mobile wireless sensor networks coverage maximization by firefly algorithm, Proceedings, 2017 International Conference on Radioelektronika, 182-186, Brno, Czech Republic, (2017).
  • Wang, G., Guo, L., Duan, H., Liu, L. and Wang, H., Dynamic deployment of wireless sensor networks by biogeography based optimization algorithm, Journal of Sensor Actuator Networks, 1, 2, 86-96, (2012).
  • He, P. and Jiang, M., Dynamic deployment of wireless sensor networks by an improved artificial bee colony algorithm, Application of Mechanical Materials, 511-512, 862-866, (2014).
  • Özdağ, R. and Karcı, A., Probabilistic dynamic distribution of wireless sensor networks with improved distribution method based on electromagnetism-like algorithm, Measurement, 79, 66-76, (2016).
  • Kukunuru, N., Thella, B. R. and Davuluri, R. L., Sensor deployment using particle swarm optimization, International Journal of Engineering Science and Technology, 2, 10, 5395-5401, (2010).
  • Shan, W. and Chen, X., Improved invasive weed optimization algorithm in sensor deployment for wireless sensor networks, Boletín Técnico, 55, 9, 310-316, (2017).
  • Liu, X. L., Zhang, X. S. and Zhu, Q. X., Enhanced fireworks algorithm for dynamic deployment of wireless sensor networks, Proceedings, 2nd International Conference on Frontiers of Sensors Technologies (ICFST), 161-165, Shenzhen, China, (2017).
  • Wang, X., Wang, S. and Ma, J., An improved co-evolutionary particle swarm optimization for wireless sensor networks with dynamic deployment, Sensors, 7, 3, 354-370, (2007).
  • Wang, L., Wu, W. H., Qi, J. Y. and Jia, Z. P., Wireless sensor network coverage optimization based on whale group algorithm, Computer Science and Information Systems, 15, 3, 569-583, (2018).
  • Farsi, M., Elhosseini, M. A., Badawy, M., Ali, H. A. and Eldin, H. Z., Deployment techniques in wireless sensor networks, coverage and connectivity: a survey, IEEE Access, 7, 28940-28954, (2019).
  • Uppal, R. S. and Kumar, S., Big bang-big crunch algorithm for dynamic deployment of wireless sensor network. International Journal of Electrical and Computer Engineering, 6, 2, 596-601, (2016).
  • Tuba, E., Tuba, M. and Simian, D., Wireless sensor network coverage problem using modified fireworks algorithm, Proceedings, 2016 International Wireless Communications and Mobile Computing Conference (IWCMC), 696-701, Paphos, Cyprus, (2016).
  • Wang, X., Wang, S. and Bi, D., Virtual force-directed particle swarm optimization for dynamic deployment in wireless sensor networks, Lecture Notes in Computer Science: Advanced Intelligent Computing Theories and Applications. With Aspects of Theoretical and Methodological Issues, International Conference on Intelligent Computing 2007, 4681, 292-303, Berlin, Germany: Springer, (2007).
  • Alia, O. M. and Al-Ajouri, A., Maximizing wireless sensor network coverage with minimum cost using harmony search algorithm, IEEE Sensors Journal, 17, 3, 882-896, (2017).
  • Karaboga, D., An idea based on honey bee swarm for numerical optimization, Technical Report-TR06, Erciyes University, Engineering Faculty, ComputerEngineering Department, Kayseri, (2005).
  • Karaboga, D., Artificial bee colony algorithm, Scholarpedia, 5, 3, 6915, (2010). http://www.scholarpedia.org/article/Artificial_bee_colony_algorithm, (25.09.2021).
  • Karaboga, D., Gorkemli, B., Ozturk, C. and Karaboga, N., A comprehensive survey: artificial bee colony (ABC) algorithm and applications, Artificial Intelligence Review, 42, 1, 21-57, (2014).
  • Yu, X., Zhang, J., Fan, J. and Zhang, T., A faster convergence artificial bee colony algorithm in sensor deployment for wireless sensor networks, International Journal of Distributed Sensor Networks, 2013 1-9, (2013).
  • Yadav, R. K., Gupta, D. and Lobiyal, D. K., Dynamic positioning of mobile sensors using modified artificial bee colony algorithm in a wireless sensor networks, International Journal of Control Theory and Applications, 10, 18, 167-176, (2017).
  • Aslan, S., Deployment in wireless sensor networks by parallel and cooperative parallel artificial bee colony algorithms, International Journal of Optimization and Control: Theories & Applications, 9, 1, 1-10, (2019).
  • Aslan, S., A transition control mechanism for artificial bee colony (ABC) algorithm, Computational Intelligence and Neuroscience, 2019, 1-24, (2019).
  • Aslan, S., Aksoy, A. and Gunay, M., Performance of parallel artificial bee colony algorithm on solving probabilistic sensor deployment problem, Proceedings, 2018 International Conference on Artificial Intelligence and Data Processing, 1-21, Malatya, Turkey, (2018).
  • Görkemli, B. and Al-Dulaimi, Z., On the performance of quick artificial bee colony algorithm for dynamic deployment of wireless sensor networks, Turkish Journal of Electrical Engineering and Computer Sciences, 27, 6, 4038-4054, (2019).
  • Karaboga, D., Gorkemli, B., A quick artificial bee colony (qABC) algorithm and its performance on optimization problems, Applied Soft Computing, 23, 227-238, (2014).
  • Mozdgir, A., Mahdavi, I., Badeleh, I. S. and Solimanpur, M., Using the Taguchi method to optimize the differential evolution algorithm parameters for minimizing the workload smoothness index in simple assembly line balancing, Mathematical and Computer Modelling, 57, 1–2, 137-151, (2013).
  • Sun, J. U., A Taguchi approach to parameter setting in a genetic algorithm for general job shop scheduling problem, Industrial Engineering and Management Systems, 6, 2, 119-124, (2007).
  • Yurtkuran, A. and Emel, E., A discrete artificial bee colony algorithm for single machine scheduling problems, International Journal of Production Research, 54, 22, 6860-6878, (2016).
  • Majumdar, A. and Ghosh, D., Genetic algorithm parameter optimization using Taguchi robust design for multi-response optimization of experimental and historical data, International Journal of Computer Applications, 127, 5, 26-32, (2015).
  • Peker M., Şen B. and Kumru P. Y., An efficient solving of the traveling salesman problem: the ant colony system having parameters optimized by the Taguchi method, Turkish Journal of Electrical Engineering and Computer Sciences, 21, 2015-2036, (2013).
  • Gümüş, D., B., Ozcan, E. and Atkin, J., An investigation of tuning a memetic algorithm for cross-domain search, Proceedings, 2016 IEEE Congress on Evolutionary Computation (CEC), 135-142, (2016).
Toplam 34 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Araştırma Makalesi
Yazarlar

Beyza Görkemli 0000-0002-8584-2308

Yayımlanma Tarihi 8 Temmuz 2022
Gönderilme Tarihi 3 Ekim 2021
Yayımlandığı Sayı Yıl 2022 Cilt: 24 Sayı: 2

Kaynak Göster

APA Görkemli, B. (2022). Parameter optimization of quick artificial bee colony algorithm for dynamic deployment problem of wireless sensor networks using Taguchi method. Balıkesir Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 24(2), 567-580.
AMA Görkemli B. Parameter optimization of quick artificial bee colony algorithm for dynamic deployment problem of wireless sensor networks using Taguchi method. BAUN Fen. Bil. Enst. Dergisi. Temmuz 2022;24(2):567-580.
Chicago Görkemli, Beyza. “Parameter Optimization of Quick Artificial Bee Colony Algorithm for Dynamic Deployment Problem of Wireless Sensor Networks Using Taguchi Method”. Balıkesir Üniversitesi Fen Bilimleri Enstitüsü Dergisi 24, sy. 2 (Temmuz 2022): 567-80.
EndNote Görkemli B (01 Temmuz 2022) Parameter optimization of quick artificial bee colony algorithm for dynamic deployment problem of wireless sensor networks using Taguchi method. Balıkesir Üniversitesi Fen Bilimleri Enstitüsü Dergisi 24 2 567–580.
IEEE B. Görkemli, “Parameter optimization of quick artificial bee colony algorithm for dynamic deployment problem of wireless sensor networks using Taguchi method”, BAUN Fen. Bil. Enst. Dergisi, c. 24, sy. 2, ss. 567–580, 2022.
ISNAD Görkemli, Beyza. “Parameter Optimization of Quick Artificial Bee Colony Algorithm for Dynamic Deployment Problem of Wireless Sensor Networks Using Taguchi Method”. Balıkesir Üniversitesi Fen Bilimleri Enstitüsü Dergisi 24/2 (Temmuz 2022), 567-580.
JAMA Görkemli B. Parameter optimization of quick artificial bee colony algorithm for dynamic deployment problem of wireless sensor networks using Taguchi method. BAUN Fen. Bil. Enst. Dergisi. 2022;24:567–580.
MLA Görkemli, Beyza. “Parameter Optimization of Quick Artificial Bee Colony Algorithm for Dynamic Deployment Problem of Wireless Sensor Networks Using Taguchi Method”. Balıkesir Üniversitesi Fen Bilimleri Enstitüsü Dergisi, c. 24, sy. 2, 2022, ss. 567-80.
Vancouver Görkemli B. Parameter optimization of quick artificial bee colony algorithm for dynamic deployment problem of wireless sensor networks using Taguchi method. BAUN Fen. Bil. Enst. Dergisi. 2022;24(2):567-80.