Eight-Figure Pattern for Enhancing the Searching Process of Grey Wolf Optimization (Eight-GWO)

Lec. Ali Hakem  Alsaeedi; Suha Muhammed Hadi; Yarub Alazzawi; Emad Badry Badry

doi:10.31185/wjps.718

Authors

Lec. Ali Hakem Alsaeedi Alqadisiyah University - College of Computer Science and Information Technology
Suha Muhammed Hadi Informatics Institute for Postgraduate Studies University of Information Technology and Communications, IRAQ
Yarub Alazzawi College of Computer Science and Information Technology, University of Al-Qadisiyah, IRAQ
Emad Badry Badry Department of Electrical Engineering, Faculty of Engineering, Suez Canal University, Ismailia, Egypt

DOI:

https://doi.org/10.31185/wjps.718

Abstract

Evolutionary algorithms suffer significantly from a stack at the local optima. This paper proposes a new strategy that detects when the search gets stuck in a local optimum and then switches to a more dynamic approach to escape. The proposed model is based on simulating eight pattern movements and embedded with a Grey Wolf Optimizer algorithm (GWO). It is called the Eight-Figure Grey Wolf Optimizer (Eight-GWO). The proposed model combines two phases: regular search when searching progresses over time while the second phase, searching by eight patterns when the algorithm reaches stuck. The Eight-pattern updates the gray position based on the sin and cos function. The proposed Eight-GWO on the 24 functions of the CEC2005 benchmark suite and compared its results with both the standard GWO and Particle Swarm Optimization (PSO). The experiments result show the proposed Eight-GWO gets better results than GWO and PSO where it achieved the best results on 80% of the test functions. The proposed Eight-GWO runs 23% faster than the original GWO and 44% faster than PSO.

References

A. H. Alsaeedi, D. Al-Shammary, S. M. Hadi, K. Ahmed, A. Ibaida, and N. AlKhazraji, "A proactive grey wolf optimization for improving bioinformatic systems with high dimensional data," International Journal of Information Technology, vol. 16, no. 8, pp. 4797-4814, 2024.

T. Bikmukhametov and J. Jäschke, "Combining machine learning and process engineering physics towards enhanced accuracy and explainability of data-driven models," Computers & Chemical Engineering, vol. 138, p. 106834, 2020.

H. Zhang and Q. Peng, "PSO and K-means-based semantic segmentation toward agricultural products," Future Generation Computer Systems, vol. 126, pp. 82-87, 2022.

R. R. Nuiaa, S. A. A. A. Alsaidi, B. K. Mohammed, A. H. Alsaeedi, Z. A. A. Alyasseri, S. Manickam, and M. A. Hussain, "Enhanced PSO Algorithm for Detecting DRDoS Attacks on LDAP Servers," International Journal of Intelligent Engineering & Systems, vol. 16, no. 5, 2023.

R. R. Nuiaa, S. Manickam, A. H. Alsaeedi, and E. S. Alomari, "Enhancing the Performance of Detect DRDoS DNS Attacks Based on the Machine Learning and Proactive Feature Selection (PFS) Model," IAENG International Journal of Computer Science, vol. 49, no. 2, 2022.

R. K. Deka, D. K. Bhattacharyya, and J. K. Kalita, "Active learning to detect DDoS attack using ranked features," Computer Communications, vol. 145, pp. 203-222, 2019.

S. M. Ali, A. H. Alsaeedi, D. Al-Shammary, H. H. Alsaeedi, and H. W. Abid, "Efficient intelligent system for diagnosis pneumonia (SARSCOVID19) in X-ray images empowered with initial clustering," Indones. J. Electr. Eng. Comput. Sci, vol. 22, no. 1, pp. 241-251, 2021.

A. H. Jabor and A. H. Ali, "Dual heuristic feature selection based on genetic algorithm and binary particle swarm optimization," Journal of University of Babylon for Pure and Applied Sciences, vol. 27, no. 1, pp. 171-183, 2019.

L. Abualigah, "RETRACTED ARTICLE: Group search optimizer: a nature-inspired meta-heuristic optimization algorithm with its results, variants, and applications," Neural Computing and Applications, vol. 33, no. 7, pp. 2949-2972, 2021.

T. Hamadneh, B. Batiha, G. M. Gharib, Z. Montazeri, F. Werner, G. Dhiman, M. Dehghani, R. K. Jawad, E. Aram, and I. K. Ibraheem, "Orangutan optimization algorithm: An innovative bio-inspired metaheuristic approach for solving engineering optimization problems," Int. J. Intell. Eng. Syst, vol. 18, no. 1, pp. 45-58, 2025.

D. Freitas, L. G. Lopes, and F. Morgado-Dias, "Particle swarm optimisation: a historical review up to the current developments," Entropy, vol. 22, no. 3, p. 362, 2020.

S. Du, W. Fan, and Y. Liu, "A novel multi-agent simulation based particle swarm optimization algorithm," Plos one, vol. 17, no. 10, p. e0275849, 2022.

G. Negi, A. Kumar, S. Pant, and M. Ram, "GWO: a review and applications," International Journal of System Assurance Engineering and Management, vol. 12, pp. 1-8, 2021.

H. Nozari and H. Abdi, "Greedy Man Optimization Algorithm (GMOA): A Novel Approach to Problem Solving with Resistant Parasites," Journal of Industrial and Systems Engineering, vol. 16, no. 3, pp. 106-117, 2024.

A. A. Heidari and P. Pahlavani, "An efficient modified grey wolf optimizer with Lévy flight for optimization tasks," Applied Soft Computing, vol. 60, pp. 115-134, 2017.

F. A. Şenel, F. Gökçe, A. S. Yüksel, and T. Yiğit, "A novel hybrid PSO–GWO algorithm for optimization problems," Engineering with Computers, vol. 35, pp. 1359-1373, 2019.

A. M. Nassef, M. A. Abdelkareem, H. M. Maghrabie, and A. Baroutaji, "The Role of Random Walk-Based Techniques in Enhancing Metaheuristic Optimization Algorithms—A Systematic and Comprehensive Review," IEEE Access, vol. 12, pp. 139573-139608, 2024, doi: 10.1109/ACCESS.2024.3466170.

B. Çavdar, E. Şahin, and E. Sesli, "On the assessment of meta-heuristic algorithms for automatic voltage regulator system controller design: a standardization process," Electrical Engineering, vol. 106, no. 5, pp. 5801-5839, 2024/10/01 2024, doi: 10.1007/s00202-024-02314-x.

Z. Ye, R. Huang, W. Zhou, M. Wang, T. Cai, Q. He, P. Zhang, and Y. Zhang, "Hybrid rice optimization algorithm inspired grey wolf optimizer for high-dimensional feature selection," Scientific Reports, vol. 14, no. 1, p. 30741, 2024/12/28 2024, doi: 10.1038/s41598-024-80648-z.

H. Mohammed, Z. Abdul, and Z. Hamad, "Enhancement of GWO for solving numerical functions and engineering problems," Neural Computing and Applications, vol. 36, no. 7, pp. 3405-3413, 2024/03/01 2024, doi: 10.1007/s00521-023-09292-4.

Z. Lyu, "State-of-the-Art Human-Computer-Interaction in Metaverse," International Journal of Human–Computer Interaction, pp. 1-19, 2023.

D. Al‐Shammary, A. L. Albukhnefis, A. H. Alsaeedi, and M. Al‐Asfoor, "Extended particle swarm optimization for feature selection of high‐dimensional biomedical data," Concurrency and Computation: Practice and Experience, vol. 34, no. 10, p. e6776, 2022.

M. Lozano and C. García-Martínez, "Hybrid metaheuristics with evolutionary algorithms specializing in intensification and diversification: Overview and progress report," Computers & Operations Research, vol. 37, no. 3, pp. 481-497, 2010.

E. P. Krishna and A. Thangavelu, "Attack detection in IoT devices using hybrid metaheuristic lion optimization algorithm and firefly optimization algorithm," International Journal of System Assurance Engineering and Management, pp. 1-14, 2021.

A. Kumar, S. Singh, and A. Kumar, "Grey wolf optimizer and other metaheuristic optimization techniques with image processing as their applications: a review," in IOP Conference Series: Materials Science and Engineering, 2021, vol. 1136, no. 1: IOP Publishing, p. 012053.

S. Mirjalili, S. M. Mirjalili, and A. Lewis, "Grey wolf optimizer," Advances in engineering software, vol. 69, pp. 46-61, 2014.

N. Hatta, A. M. Zain, R. Sallehuddin, Z. Shayfull, and Y. Yusoff, "Recent studies on optimisation method of Grey Wolf Optimiser (GWO): a review (2014–2017)," Artificial intelligence review, vol. 52, pp. 2651-2683, 2019.