511 391
Full Length Article
Fusion: Practice and Applications
Volume 13 , Issue 1, PP: 19-36 , 2023 | Cite this article as | XML | Html |PDF

Title

Fusion of Water Evaporation Optimization and Great Deluge: A Dynamic Approach for Benchmark Function Solving

  Saman M. Almufti 1 *

1  Nawroz University, College of Science, Computer Science Department, Duhok, Iraq
    (Saman.Almofty@gmail.com)


Doi   :   https://doi.org/10.54216/FPA.130102

Received: March 07, 2023 Revised: June 03, 2023 Accepted: August 09, 2023

Abstract :

The "Water Evaporation Optimization - Great Deluge" explores the synergy between the Water Evaporation Optimization Algorithm (WEOA) and the Great Deluge Algorithm (GDA) to create a novel fusion model. This research investigates the efficacy of combining these two powerful optimization techniques in addressing benchmark problems. The fusion model incorporates WEOA's dynamic exploration-exploitation dynamics and GDA's global search capabilities. By merging their strengths, the fusion model seeks to enhance convergence efficiency and solution quality. The study presents an experimental analysis of the fusion model's performance across a range of benchmark functions, evaluating its ability to escape local optima and converge towards global optima. The results provide insights into the effectiveness of the fusion model and its potential for addressing complex optimization challenges., a comprehensive performance analysis of the application of the proposed fusion model to a curated set of widely acknowledged benchmark functions, renowned for their role in evaluating the capabilities of optimization algorithms, is undertaken. By rigorously evaluating the convergence characteristics, solution quality, and computational efficiency of the algorithm, a thorough understanding of the strengths and limitations of WEOA is aimed to be provided. Through meticulous comparisons with established optimization techniques, illumination of the aptitude of WEOA in addressing diverse optimization challenges across a spectrum of problem landscapes is intended. The analytical insights, not only advancing the understanding of WEOA's applicability, but also furnishing valuable guidance for both researchers and practitioners in search of robust optimization methodologies, are proffered.

Keywords :

Water Evaporation Optimization Algorithm; Metaheuristic Algorithms; Benchmark Functions; Fusion Model.

References :

[1] A. Kaveh and T. Bakhshpoori, Metaheuristics: Outlines, MATLAB Codes and Examples. Cham: Springer International Publishing, 2019. doi: 10.1007/978-3-030-04067-3.

[2] S. M. Almufti, “Vibrating Particles System Algorithm performance in solving Constrained Optimization Problem,” Academic Journal of Nawroz University, vol. 11, no. 3, pp. 231–242, Aug. 2022, doi: 10.25007/ajnu.v11n3a1499.

[3] R. Asaad and N. Abdulnabi, “Using Local Searches Algorithms with Ant Colony Optimization for the Solution of TSP Problems,” Academic Journal of Nawroz University, vol. 7, no. 3, pp. 1–6, 2018, doi: 10.25007/ajnu.v7n3a193.

[4] S. M. Almufti, R. Boya Marqas, and R. R. Asaad, “Comparative study between elephant herding optimization (EHO) and U-turning ant colony optimization (U-TACO) in solving symmetric traveling salesman problem (STSP),” Journal of Advanced Computer Science & Technology, vol. 8, no. 2, p. 32, Aug. 2019, doi: 10.14419/jacst.v8i2.29403.

[5] S. M. Almufti, A. Ahmad Shaban, R. Ismael Ali, and J. A. Dela Fuente, “Overview of Metaheuristic Algorithms,” Polaris Global Journal of Scholarly Research and Trends, vol. 2, no. 2, pp. 10–32, Apr. 2023, doi: 10.58429/pgjsrt.v2n2a144.

[6] S. M. Almufti, “Historical survey on metaheuristics algorithms,” International Journal of Scientific World, vol. 7, no. 1, p. 1, Nov. 2019, doi: 10.14419/ijsw.v7i1.29497.

[7] Y. Shi and R. Eberhart, “A modified particle swarm optimizer,” in 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360), IEEE, pp. 69–73. doi: 10.1109/ICEC.1998.699146.

[8] R. Eberhart and J. Kennedy, “A new optimizer using particle swarm theory,” in MHS’95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science, 1995, pp. 39–43. doi: 10.1109/MHS.1995.494215.

[9] C. A. C. Coello and M. Montes, “Constraint-handling in genetic algorithms through the use of dominance-based tournament selection.” [Online]. Available: www.elsevier.com/locate/aei

[10] S. M. Almufti, A. Yahya Zebari, and H. Khalid Omer, “A comparative study of particle swarm optimization and genetic algorithm,” Journal of Advanced Computer Science & Technology, vol. 8, no. 2, p. 40, Oct. 2019, doi: 10.14419/jacst.v8i2.29401.

[11] M. Zhang, W. Luo, and X. Wang, “Differential evolution with dynamic stochastic selection for constrained optimization,” Inf Sci (N Y), vol. 178, no. 15, pp. 3043–3074, Aug. 2008, doi: 10.1016/j.ins.2008.02.014.

[12] K. Deb, “Optimal design of a welded beam via genetic algorithms,” AIAA Journal, vol. 29, no. 11, pp. 2013–2015, Nov. 1991, doi: 10.2514/3.10834.

[13] C. ZHANG and H.-P. (BEN) WANG, “MIXED-DISCRETE NONLINEAR OPTIMIZATION WITH SIMULATED ANNEALING,” Engineering Optimization, vol. 21, no. 4, pp. 277–291, Sep. 1993, doi: 10.1080/03052159308940980.

[14] J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proceedings of ICNN’95 - International Conference on Neural Networks, IEEE, pp. 1942–1948. doi: 10.1109/ICNN.1995.488968.

[15] S. M. Almufti, “Hybridizing Ant Colony Optimization Algorithm for Optimizing Edge-Detector Techniques,” Academic Journal of Nawroz University, vol. 11, no. 2, pp. 135–145, May 2022, doi: 10.25007/ajnu.v11n2a1320.

[16] A. Acan and A. Ünveren, “A great deluge and tabu search hybrid with two-stage memory support for quadratic assignment problem,” Appl Soft Comput, vol. 36, pp. 185–203, Nov. 2015, doi: 10.1016/j.asoc.2015.06.061.

[17] S. Almufti, “The novel Social Spider Optimization Algorithm: Overview, Modifications, and Applications,” ICONTECH INTERNATIONAL JOURNAL, vol. 5, no. 2, pp. 32–51, Jun. 2021, doi: 10.46291/icontechvol5iss2pp32-51.

[18] X.-S. Yang, “Harmony Search as a Metaheuristic Algorithm,” in Music-Inspired Harmony Search Algorithm, Berlin, Heidelberg: Springer Berlin Heidelberg, pp. 1–14. doi: 10.1007/978-3-642-00185-7_1.

[19] H. T. Sadeeq, A. M. Abdulazeez, N. A. Kako, D. A. Zebari, and D. Q. Zeebaree, “A New Hybrid Method for Global Optimization Based on the Bird Mating Optimizer and the Differential Evolution,” in 2021 7th

International Engineering Conference “Research & Innovation amid Global Pandemic" (IEC), IEEE, Feb. 2021, pp. 54–60. doi: 10.1109/IEC52205.2021.9476147.

[20] I. Fister, I. Fister, X.-S. Yang, and J. Brest, “A comprehensive review of firefly algorithms,” Swarm Evol Comput, vol. 13, pp. 34–46, Dec. 2013, doi: 10.1016/j.swevo.2013.06.001.

[21] X.-S. Yang and Suash Deb, “Cuckoo Search via Levy flights,” in 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC), IEEE, 2009, pp. 210–214. doi: 10.1109/NABIC.2009.5393690.

[22] S. M. Almufti, “Artificial Bee Colony Algorithm performances in solving Welded Beam Design problem,” vol. 28, doi: 10.24297/j.cims.2022.12.17.

[23] S. M. Almufti, A. A. H. Alkurdi, and E. A. Khoursheed, “Artificial Bee Colony Algorithm Performances in Solving Constraint-Based Optimization Problem,” vol. 21, p. 2022.

[24] S. M. Ahmad, H. B. Marqas, and R. B. Asaad, “Grey wolf optimizer: Overview, modifications and applications,” International Research Journal of Science, vol. 1, no. 1, pp. 44–56, 2021, doi: 10.5281/zenodo.5195644.

[25] S. Mirjalili, S. M. Mirjalili, and A. Lewis, “Grey Wolf Optimizer,” Advances in Engineering Software, vol. 69, pp. 46–61, Mar. 2014, doi: 10.1016/j.advengsoft.2013.12.007.

[26] A. Yahya Zebari, S. M. Almufti, and C. Mohammed Abdulrahman, “Bat algorithm (BA): review, applications and modifications,” International Journal of Scientific World, vol. 8, no. 1, p. 1, Jan. 2020, doi: 10.14419/ijsw.v8i1.30120.

[27] A. W. Marashdih, Z. F. Zaaba, S. M. Almufti, and Z. Fitri Zaaba, “The Problems and Challenges of Infeasible Paths in Static Analysis Bat Algorithm (BA): Literature Review various types and its Applications View project Hybrid Metaheuristic in solving NP-Hard Problem View project The Problems and Challenges of Infeasible Paths in Static Analysis,” International Journal of Engineering & Technology, pp. 412–417, 2018, doi: 10.14419/ijet.v7i4.19.23175.

[28] A. Hasan Bdair Aighuraibawi et al., “Feature Selection for Detecting ICMPv6-Based DDoS Attacks Using Binary Flower Pollination Algorithm,” Computer Systems Science and Engineering, vol. 47, no. 1, pp. 553–574, 2023, doi: 10.32604/csse.2023.037948.

[29] S. M. Almufti, “Lion algorithm: Overview, modifications and applications E I N F O,” International Research Journal of Science, vol. 2, no. 2, pp. 176–186, 2022, doi: 10.5281/zenodo.6973555.

[30] S. Mirjalili and A. Lewis, “The Whale Optimization Algorithm,” Advances in Engineering Software, vol. 95, pp. 51–67, May 2016, doi: 10.1016/j.advengsoft.2016.01.008.

[31] S. Almufti, “Vibrating Particles System Algorithm: Overview, Modifications and Applications,” ICONTECH INTERNATIONAL JOURNAL, vol. 6, no. 3, pp. 1–11, Sep. 2022, doi: 10.46291/icontechvol6iss3pp1-11.

[32] C. Junyue, D. Q. Zeebaree, C. Qingfeng, and D. A. Zebari, “Breast cancer diagnosis using hybrid AlexNet-ELM and chimp optimization algorithm evolved by Nelder-mead simplex approach,” Biomed Signal Process Control, vol. 85, p. 105053, Aug. 2023, doi: 10.1016/j.bspc.2023.105053.

[33] Xin-She Yang and Suash Deb, “Engineering optimisation by cuckoo search,” Int. J. Mathematical Modelling and Numerical Optimisation, vol. 1, no. 4, pp. 330–343, 2010.

[34] A. H. Gandomi, X.-S. Yang, and A. H. Alavi, “Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems,” Eng Comput, vol. 29, no. 1, pp. 17–35, Jan. 2013, doi: 10.1007/s00366-011-0241-y.

[35] A. H. Gandomi, X.-S. Yang, and A. H. Alavi, “Mixed variable structural optimization using Firefly Algorithm,” Comput Struct, vol. 89, no. 23–24, pp. 2325–2336, Dec. 2011, doi: 10.1016/j.compstruc.2011.08.002.

[36] Mohammad-Javad Kazemzadeh-Parsi, “A modified firefly algorithm for engineering design optimization problems,” Iranian Journal of Science and Technology Transactions of Mechanical Engineering, vol. 38, no. 2, pp. 403–421, 2014.

[37] L. Abualigah, D. Yousri, M. Abd Elaziz, A. A. Ewees, M. A. A. Al-qaness, and A. H. Gandomi, “Aquila Optimizer: A novel meta-heuristic optimization algorithm,” Comput Ind Eng, vol. 157, Jul. 2021, doi: 10.1016/j.cie.2021.107250.

[38] A. Kaveh and T. Bakhshpoori, “Water Evaporation Optimization: A novel physically inspired optimization algorithm,” Comput Struct, vol. 167, pp. 69–85, Apr. 2016, doi: 10.1016/j.compstruc.2016.01.008.

[39] A. Kaveh, “Water Evaporation Optimization Algorithm,” in Advances in Metaheuristic Algorithms for Optimal Design of Structures, Cham: Springer International Publishing, 2017, pp. 489–509. doi: 10.1007/978-3-319-46173-1_16.

[40] A. Kaveh and T. Bakhshpoori, “Water Evaporation Optimization: A novel physically inspired optimization algorithm,” Comput Struct, vol. 167, pp. 69–85, Apr. 2016, doi: 10.1016/j.compstruc.2016.01.008.

[41] Ajith Krishna R, Ankit Kumar, Vijay K, An Automated Optimize Utilization of Water and Crop Monitoring in Agriculture Using IoT, Journal of Cognitive Human-Computer Interaction, Vol. 1 , No. 1 , (2021) : 37-45 (Doi : https://doi.org/10.54216/JCHCI.010105).

[42] A. Kaveh and T. Bakhshpoori, “A new metaheuristic for continuous structural optimization: water evaporation optimization,” Structural and Multidisciplinary Optimization, vol. 54, no. 1, pp. 23–43, Jul. 2016, doi: 10.1007/s00158-015-1396-8.

[43] Mohamed Saber, El-Sayed M. El-Kenawy, Abdelhameed Ibrahim, Marwa M. Eid, Watermarking System for Medical Images Using Optimization Algorithm, Fusion: Practice and Applications, Vol. 10 , No. 1 , (2023) : 89-99 (Doi : https://doi.org/10.54216/FPA.100105)

[44] Gopal Chaudhary, Puneet Singh Lamba, Deepali Virmani, A Proposed Optimization Model for Water Quality Prediction in Internet of Things Environment, Journal of Intelligent Systems and Internet of Things, Vol. 6 , No. 2 , (2022) : 32-44 (Doi : https://doi.org/10.54216/JISIoT.060203)

[45] J. Kudela and R. Matousek, “New Benchmark Functions for Single-Objective Optimization Based on a Zigzag Pattern,” IEEE Access, vol. 10, pp. 8262–8278, 2022, doi: 10.1109/ACCESS.2022.3144067.

[46] Taif Khalid Shakir,Ahmed N. Al Masri, Single Valued Neutrosophic Set for Selection of Water Supply in Intelligent Farming, International Journal of Advances in Applied Computational Intelligence, Vol. 2 , No. 2 , (2022) : 37-44 (Doi : https://doi.org/10.54216/IJAACI.020204)

[47] A. H. Gandomi, X.-S. Yang, A. H. Alavi, and S. Talatahari, “Bat algorithm for constrained optimization tasks,” Neural Comput Appl, vol. 22, no. 6, pp. 1239–1255, May 2013, doi: 10.1007/s00521-012-1028-9.

[48] E. Mezura-Montes and C. A. C. Coello, “An empirical study about the usefulness of evolution strategies to solve constrained optimization problems,” Int J Gen Syst, vol. 37, no. 4, pp. 443–473, Aug. 2008, doi: 10.1080/03081070701303470.

[49] C. A. Coello Coello, “Use of a self-adaptive penalty approach for engineering optimization problems,” Comput Ind, vol. 41, no. 2, pp. 113–127, Mar. 2000, doi: 10.1016/S0166-3615(99)00046-9.

[50] Y. Y. AO and H. Q. CHI, “An Adaptive Differential Evolution Algorithm to Solve Constrained Optimization Problems in Engineering Design,” Engineering, vol. 02, no. 01, pp. 65–77, 2010, doi: 10.4236/eng.2010.21009.

[51] Y. Belkourchia, L. Azrar, and E.-S. M. Zeriab, “A Hybrid Optimization Algorithm for Solving Constrained Engineering Design Problems,” in 2019 5th International Conference on Optimization and Applications (ICOA), IEEE, Apr. 2019, pp. 1–7. doi: 10.1109/ICOA.2019.8727654.

[52] A. Kaveh, S. Talatahari, and S. Talatahari, “Engineering optimization withhybrid particle swarm and ant colony optimization Set theoretical framework for meta-heuristic optimization algorithm View project

ENGINEERING OPTIMIZATION WITH HYBRID PARTICLE SWARM AND ANT COLONY OPTIMIZATION,” 2009. [Online]. Available: https://www.researchgate.net/publication/228667380

[53] C. A. Coello Coello and E. Mezura Montes, “Constraint-handling in genetic algorithms through the use of dominance-based tournament selection,” Advanced Engineering Informatics, vol. 16, no. 3, pp. 193–203, Jul. 2002, doi: 10.1016/S1474-0346(02)00011-3.

[54] E. Sandgren, “Nonlinear Integer and Discrete Programming in Mechanical Design Optimization,” Journal of Mechanical Design, vol. 112, no. 2, pp. 223–229, Jun. 1990, doi: 10.1115/1.2912596.

[55] C. A. Coello Coello, “Use of a self-adaptive penalty approach for engineering optimization problems,” Comput Ind, vol. 41, no. 2, pp. 113–127, Mar. 2000, doi: 10.1016/S0166-3615(99)00046-9.

[56] S. M. Almufti, “Artificial Bee Colony Algorithm performances in solving Welded Beam Design problem,” Computer Integrated Manufacturing Systems, vol. 28, no. 12, 2022, doi: 10.24297/j.cims.2022.12.17.

[57] A.-R. Hedar and M. Fukushima, “Derivative-Free Filter Simulated Annealing Method for Constrained Continuous Global Optimization,” Journal of Global Optimization, vol. 35, no. 4, pp. 521–549, Aug. 2006, doi: 10.1007/s10898-005-3693-z.

[58] K. S. Lee and Z. W. Geem, “A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice,” Comput Methods Appl Mech Eng, vol. 194, no. 36–38, pp. 3902–3933, Sep. 2005, doi: 10.1016/j.cma.2004.09.007.

[59] Algamal, Z. Y., Abonazel, M. R., & Lukman, A. F. (2023). Modified Jackknife Ridge Estimator for Beta Regression Model With Application to Chemical Data. International Journal of Mathematics, Statistics, and Computer Science, 1, 15–24. https://doi.org/10.59543/ijmscs.v1i.7713

[60] Obeid, N. (2023). On The Product and Ratio of Pareto and Erlang Random Variables. International Journal of Mathematics, Statistics, and Computer Science, 1, 33–47. https://doi.org/10.59543/ijmscs.v1i.7737


Cite this Article as :
Style #
MLA Saman M. Almufti. "Fusion of Water Evaporation Optimization and Great Deluge: A Dynamic Approach for Benchmark Function Solving." Fusion: Practice and Applications, Vol. 13, No. 1, 2023 ,PP. 19-36 (Doi   :  https://doi.org/10.54216/FPA.130102)
APA Saman M. Almufti. (2023). Fusion of Water Evaporation Optimization and Great Deluge: A Dynamic Approach for Benchmark Function Solving. Journal of Fusion: Practice and Applications, 13 ( 1 ), 19-36 (Doi   :  https://doi.org/10.54216/FPA.130102)
Chicago Saman M. Almufti. "Fusion of Water Evaporation Optimization and Great Deluge: A Dynamic Approach for Benchmark Function Solving." Journal of Fusion: Practice and Applications, 13 no. 1 (2023): 19-36 (Doi   :  https://doi.org/10.54216/FPA.130102)
Harvard Saman M. Almufti. (2023). Fusion of Water Evaporation Optimization and Great Deluge: A Dynamic Approach for Benchmark Function Solving. Journal of Fusion: Practice and Applications, 13 ( 1 ), 19-36 (Doi   :  https://doi.org/10.54216/FPA.130102)
Vancouver Saman M. Almufti. Fusion of Water Evaporation Optimization and Great Deluge: A Dynamic Approach for Benchmark Function Solving. Journal of Fusion: Practice and Applications, (2023); 13 ( 1 ): 19-36 (Doi   :  https://doi.org/10.54216/FPA.130102)
IEEE Saman M. Almufti, Fusion of Water Evaporation Optimization and Great Deluge: A Dynamic Approach for Benchmark Function Solving, Journal of Fusion: Practice and Applications, Vol. 13 , No. 1 , (2023) : 19-36 (Doi   :  https://doi.org/10.54216/FPA.130102)