Performance Analysis of Source-Linked Harmony Search Algorithm for Big Data Optimization

Öz

In this study, the performance of Source-Linked Harmony Search algorithm (slinkHSA) that is a new variant of the Harmony Search algorithm (HSA) powered with the usage of existing data for generating initial solutions was investigated over a big data optimization problem requiring minimization of measurement noise for electroencephalography (EEG) signals. The results obtained by the mentioned HSA variant were also compared to the results of other meta-heuristic techniques. Comparative studies showed that generating initial harmonies by guiding the existing EEG signals significantly contributes to the qualities of the solutions and increases the convergence speed of the algorithm.

Anahtar Kelimeler

• Harmony Search Algorithm
• Source-linked Harmony Search Algorithm
• Big Data Optimisation

Büyük Veri Optimizasyonu için Kaynak-Bağlantılı Harmoni Arama Algoritmasının Performans Analizi

Öz

Bu çalışmada, Harmoni Arama algoritmasının (Harmony Search algorithm, HSA) mevcut veriden faydalanarak başlangıç çözümlerini üretme yaklaşımı ile güçlendirilmiş varyantı olan Kaynak-Bağlantılı Harmoni Arama algoritmasının (Source-Linked HSA, slinkHSA) performansı elektroensefalografi (EEG) sinyallerinde gürültü minimizasyonu gerektiren büyük veri optimizasyonu üzerinden incelenmiştir. slinkHSA ile elde edilen sonuçlar diğer meta-sezgisel teknikler tarafından bulunan sonuçlar üzerinden kıyaslanmıştır. Karşılaştırmalar, başlangıç harmonilerini EEG sinyalleri kullanılarak üretmenin çözümlerinin kalitesini önemli ölçüde katkıda bulunduğunu ve algoritmanın yakınsama hızını artırdığını göstermiştir.

Anahtar Kelimeler

• Harmoni Arama Algoritması
• Kaynak-Bağlantılı HSA
• Büyük Veri Optimizasyonu
• EEG
• Harmony Search Algorithm
• Source-linked Harmony Search Algorithm
• Big Data Optimisation

Kaynakça

1. Kambatla, K., Kollias G.,Kumar V., Grama, A., Trends in Big Data Analytics. Journal of Parallel and Distributed Computing, 74(7):2561–2573, 2014.
2. Gudivada, V. N., Baeza-Yates, R., Raghavan, V. V., Big Data: Promises and Problems. Computer, 48(3):20–23, 2015.
3. Tsai, C. W., Lai, C. F., Chao, H. C. , Vasilakos, A. V., Big data Analytics: A Survey. Journal of Big Data, 2(1):21, 2015.
4. Özköse, H., Arı, E. S., Gencer, C., Yesterday, Today and Tomorrow of Big Data, Procedia-Social and Behavioral Sciences, 195, 1042-1050, 2015
5. Abbass, H. A., Calibrating Independent Component Analysis with Laplacian Reference for Real-Time EEG Artifact Removal. International Conference on Neural Information Processing, pages 68–75, 2014.
6. Goh, S. K., Abbass, H. A., Tan, K. C., Al-Mamun, A., Artifact Removal From EEG Using a Multi-Objective Independent Component Analysis Model. International Conference on Neural Information Processing, pages 570–577, 2014.
7. Goh, S. K., Tan, K. C., Al-Mamun, A., Abbass, H. A., Evolutionary Big Optimization (bigopt) of Signals. IEEE Congress on Evolutionary Computation (CEC), pp: 3332–3339. IEEE, 2015.
8. Zhang, Y., Zhou, M., Jiang, Z., Liu, J., A Multi-Agent Genetic Algorithm for Big Optimization Problems. IEEE Congress on Evolutionary Computation (CEC), pages 703–707. IEEE, 2015.

9. Zhang, Y., Liu, J., Zhou, M., Jiang, Z., A Multi-Objective Memetic Algorithm Based on Decomposition for Big Optimization Problems. Memetic Computing, 8(1):45–61, 2016.
10. Elsayed, S., Sarker, R., An Adaptive Configuration of Differential Evolution Algorithms for Big Data. IEEE Congress on Evolutionary Computation (CEC). IEEE, pp: 695–702, 2015.
11. Elsayed, S., Sarker, R., Differential Evolution Framework for Big Data Optimization. Memetic Computing, 8(1):17–33, 2016.
12. El Majdouli, M. A., Bougrine, S., Rbouh, I., El Imrani, A. A., A Fireworks Algorithm for Single Objective Big Optimization of Signals. IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA), pp: 1–7. IEEE, 2016.
13. Loukdache A., El Majdouli, M. A., Bougrine, S., El Imrani, A. A., A Clonal Selection Algorithm For the Electro Encephalography Signals Reconstruction.International Conference on Electrical and Information Technologies (ICEIT), pp: 1–6. IEEE, 2017.
14. Meselhi, M. A., Elsayed, S. M., Essam, D. L., Sarker, R. A. Fast Differential Evolution for Big Optimization. 2017 11th International Conference on Software, Knowledge, Information Management and Applications (SKIMA), pp: 1–6. IEEE, 2017.
15. Wang, H., Wang, W., Cui, L., Sun, H., Zhao, J., Wang, Y., Xue, Y., A Hybrid Multiobjective Firefly Algorithm for Big Data Optimization. Applied Soft Computing, 69:806–815, 2018.
16. Yi, J. H., Deb, S., Dong, J., Alavi, A. H., Wang, G. G., An Improved NSGA-III Algorithm with Adaptive Mutation Operator for Big Data Optimization Problems. Future Generation Computer Systems, 88:571–585, 2018.
17. Aslan, S., An Artificial Bee Colony-Guided Approach for Electro-Encephalography Signal Decomposition-Based Big Data Optimization. International Journal of Information Technology & Decision Making, 19(02), 561-600, 2020.
18. Jiang, X., Bian, G., Tian, Z., Removal of Artifacts from EEG Signals: A Review. Sensors, 19(5), 987, 2019
19. Geem, Z. W., Kim, J. H., Loganathan, G. V., A New Heuristic Optimization Algorithm: Harmony Search. Simulation, 76(2), 60-68, 2001.
20. Srinivas, M., Patnaik, L. M., Genetic Algorithms: A Survey. Computer, vol. 27, no. 6, pp. 17–26, Jun. 1994.
21. Price, K. V., Differential Evolution, Handbook of Optimization. Berlin, Germany: Springer, 2013, pp. 187–214.
22. Karaboga, D., Basturk, B, A Powerful and Efficient Algorithm for Numerical Function Optimization: Artificial Bee Colony (ABC) Algorithm. J. Global Optim., vol. 39, no. 3, pp. 459–471, Oct. 2007.
23. Shi, Y., Particle Swarm Optimization: Developments, Applications and Resources, Proc. Congr. Evol. Comput., vol. 1, May 2001, pp. 81–86.
24. Aslan, S, Demirci, S., Immune Plasma Algorithm: A Novel Meta-Heuristic for Optimization Problems. IEEE Access, vol. 8, pp. 220227-220245, 2020
25. İleri, S. C., Aslan, S., Demirci, S., A Novel Harmony Search Based Method for Noise Minimization on EEG Signals. 2021 6th International Conference on Computer Science and Engineering (UBMK), 2021, pp. 747-750