A Novel Modified Swarm Intelligence Algorithm Combining Black Widow Optimization Algorithm and Pelican Optimization Algorithm to solve Global Optimization Problems

Ayad Hamad Khalaf

doi:10.47134/ppm.v2i2.1480

Authors

Ayad Hamad Khalaf Tikrit University

DOI:

https://doi.org/10.47134/ppm.v2i2.1480

Keywords:

Swarm Intelligence, Optimization, Meta-heuristic Algorithm, Modified, Intelligent Technologies

Abstract

In this paper, an improved algorithm called BWOA-POA is a hybrid algorithm based on the Black Widow Algorithm (BWOA), which is an algorithm inspired by nature and has excellent specifications in addition to another algorithm, the Pelican Swarm Optimization Algorithm (POA), which is a smart swarm algorithm that is also inspired by nature When studying these two algorithms, we find that each of them has some weaknesses and that they fall into local solutions in some countries and this is what prompted us to develop the hybrid algorithm BWOA- POA, which was able to avoid falling into the trap of local solutions and reach the global optimal solution, as the numerical results proved its superiority over the others and the speed of reaching the solution in record time with the least number of swarm elements and the least number of iterations, as this developed algorithm BWOA-POA was applied to the optimality measurement functions and the results were excellent if compared with its predecessors, This model is one of the most powerful models and can be applied in solving engineering problems and all studies that need to reach the best solutions from minimizing or maximizing the models presented.

References

Abualigah, L. (2021). The Arithmetic Optimization Algorithm. Computer Methods in Applied Mechanics and Engineering, 376, ISSN 0045-7825, https://doi.org/10.1016/j.cma.2020.113609 DOI: https://doi.org/10.1016/j.cma.2020.113609

Anderson, J.G. Foraging behavior of the American white pelican (Pelecanus erythrorhyncos) in western Nevada. Colonial Waterbirds 1991, 14, 166–172. DOI: https://doi.org/10.2307/1521506

Baruffaldi L. and Andrade M., (2015) " Contact Pheromones Mediate Male Preference in Black Widow Spiders: Avoidance of Hungry Sexual Cannibals " Elsevier, Animal Behaviour, vol. 102, pp. 25–32. DOI: https://doi.org/10.1016/j.anbehav.2015.01.007

Braik, M. (2022). White Shark Optimizer: A novel bio-inspired meta-heuristic algorithm for global optimization problems. Knowledge-Based Systems, 243, ISSN 0950-7051, https://doi.org/10.1016/j.knosys.2022.108457 DOI: https://doi.org/10.1016/j.knosys.2022.108457

Braik, M.S. (2021). Chameleon Swarm Algorithm: A bio-inspired optimizer for solving engineering design problems. Expert Systems with Applications, 174, ISSN 0957-4174, https://doi.org/10.1016/j.eswa.2021.114685 DOI: https://doi.org/10.1016/j.eswa.2021.114685

Chou, J.S. (2021). A novel metaheuristic optimizer inspired by behavior of jellyfish in ocean. Applied Mathematics and Computation, 389, ISSN 0096-3003, https://doi.org/10.1016/j.amc.2020.125535 DOI: https://doi.org/10.1016/j.amc.2020.125535

Delgado A., Vaquez H., Covarrubias J., Cruz N., Garcıa-Vite P., Morales-Cepeda A., and Ramirez-Arredondo J., (2020) " A Novel Bio-Inspired Algorithm Applied to Selective Harmonic Elimination in a Three-Phase Eleven-Level Inverter " Hindawi, Mathematical Problems in Engineering, vol. 2020, pp. 1-10. DOI: https://doi.org/10.1155/2020/8856040

Erik Cuevas, Fernando Fausto, Adrián González “New Advancements in Swarm Algorithms: Operators and Applications”, 2020, Volume 160, ISBN: 978-3-030-16338-9.

Hamad Khalaf, Ayad, and Dr Ban Ahmed Mitras. "Using Modified Conjugate Gradient Method to Improve SCA." Journal of Physics Conference Series. Vol. 1591. No. 1. 2020. DOI: https://doi.org/10.1088/1742-6596/1591/1/012050

Hashim, F.A. (2022). Honey Badger Algorithm: New metaheuristic algorithm for solving optimization problems. Mathematics and Computers in Simulation, 192, 84-110, ISSN 0378-4754, https://doi.org/10.1016/j.matcom.2021.08.013 DOI: https://doi.org/10.1016/j.matcom.2021.08.013

Hasmat Malik, Atif Iqbal, Puneet Joshi, Sanjay Agrawal, Farhad Ilahi Bakhsh, (2021), “Metaheuristic and Evolutionary Computation: Algorithms and Applications”, SCI, volume 916. DOI: https://doi.org/10.1007/978-981-15-7571-6

Hayyolalam, Vahideh, and Ali Asghar Pourhaji Kazem. "Black widow optimization algorithm: a novel meta-heuristic approach for solving engineering optimization problems." Engineering Applications of Artificial Intelligence 87 (2020): 103249. DOI: https://doi.org/10.1016/j.engappai.2019.103249

Khalaf, Ayad Hamad; MITRAS, Ban Ahmed. Two-hybrid sine cosine algorithm based on Invasive weed optimization algorithm and Bat algorithm. In: IOP Conference Series: Materials Science and Engineering. IOP Publishing, 2021. p. 012053. DOI: https://doi.org/10.1088/1757-899X/1058/1/012053

Louchart, A.; Tourment, N.; Carrier, J. The earliest known pelican reveals 30 million years of evolutionary stasis in beak morphology. J. Ornithol. 2010, 152, 15–20. DOI: https://doi.org/10.1007/s10336-010-0537-5

Marwa Waleed Hamad Salah, Ban Ahmed Hasan (2022) " Hybrid Meta-Heuristics Algorithms Using Unconstrained Optimization for Text Processing" Thesis in University of Mosul.

Mirjalili, S. (2016). "SCA: A Sine Cosine Algorithm for solving optimization problems", Knowledge Based Systems, 96, pp120 - 133. DOI: https://doi.org/10.1016/j.knosys.2015.12.022

Perrins, C.M.; Middleton, A.L. The Encyclopaedia of Birds; Guild Publishing: London, UK, 1985; pp. 53–54.

Seyyedabbasi, A. (2023). Sand Cat swarm optimization: a nature-inspired algorithm to solve global optimization problems. Engineering with Computers, 39, 2627-2651, ISSN 0177-0667, https://doi.org/10.1007/s00366-022-01604-x DOI: https://doi.org/10.1007/s00366-022-01604-x

Su, H. (2023). RIME: A physics-based optimization. Neurocomputing, 532, 183-214, ISSN 0925-2312, https://doi.org/10.1016/j.neucom.2023.02.010 DOI: https://doi.org/10.1016/j.neucom.2023.02.010

Trojovský, Pavel, and Mohammad Dehghani. "Pelican optimization algorithm: A novel nature-inspired algorithm for engineering applications." Sensors 22.3 (2022): 855. DOI: https://doi.org/10.3390/s22030855

Xue, J. (2023). Dung beetle optimizer: a new meta-heuristic algorithm for global optimization. Journal of Supercomputing, 79(7), 7305-7336, ISSN 0920-8542, https://doi.org/10.1007/s11227-022-04959-6 DOI: https://doi.org/10.1007/s11227-022-04959-6

Zamani, H. (2022). Starling murmuration optimizer: A novel bio-inspired algorithm for global and engineering optimization. Computer Methods in Applied Mechanics and Engineering, 392, ISSN 0045-7825, https://doi.org/10.1016/j.cma.2022.114616 DOI: https://doi.org/10.1016/j.cma.2022.114616

Zhao, S. (2022). Dandelion Optimizer: A nature-inspired metaheuristic algorithm for engineering applications. Engineering Applications of Artificial Intelligence, 114, ISSN 0952-1976, https://doi.org/10.1016/j.engappai.2022.105075 DOI: https://doi.org/10.1016/j.engappai.2022.105075

Zhao, W. (2022). Artificial hummingbird algorithm: A new bio-inspired optimizer with its engineering applications. Computer Methods in Applied Mechanics and Engineering, 388, ISSN 0045-7825, https://doi.org/10.1016/j.cma.2021.114194 DOI: https://doi.org/10.1016/j.cma.2021.114194

Zhong, C. (2022). Beluga whale optimization: A novel nature-inspired metaheuristic algorithm. Knowledge-Based Systems, 251, ISSN 0950-7051, https://doi.org/10.1016/j.knosys.2022.109215 DOI: https://doi.org/10.1016/j.knosys.2022.109215