Buzzard Optimization Algorithm: A Nature-Inspired Metaheuristic Algorithm

  • Ali Arshaghi Department of Electrical Engineering, Central Tehran Branch, Islamic Azad University, Tehran
  • Mohsen Ashourian Department of Electrical Engineering, Majlesi Branch, Islamic Azad University
  • Leila Ghabeli Department of Electrical Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran
Keywords: Buzzard Optimization Algorithm, Global Optimization, Benchmark, Bio Inspired Meta-Heuristic

Abstract

Various algorithms have proposed during the last decade for solving different complex optimization problems. The meta-heuristic algorithms have been highly noted among researchers. In this paper, a new algorithm, known as the Buzzards Optimization Algorithm (BUZOA), is introduced. Marvelous and special lifestyle of buzzards and their competition characteristics for prey has been the basic motivation for this new optimization algorithm. The algorithm performance has been compared with newest and well-known meta-heuristics on some benchmark problems and test functions. Results have shown the high performance of the proposed BUZOA compared to the other well known algorithms.

References

[1] K. Suresh, N. Kumarappan: Hybrid improved binary particle swarm optimization approach for generation maintenance scheduling problem. Swarm and Evolutionary Computation. 9, 69-89, (2013.
[2] S. M. Goldansaz, F. Jolai, and A.H.Z. Anaraki: A hybrid imperialist competitive algorithm for minimizing makespan in a multiprocessor open shop. Applied Mathematical Modelling. 37, 9603-9616, (2013.
[3] G. a. A. G. Fornarelli: An unsupervised multi-swarm clustering technique for image segmentation. Swarm and Evolutionary Computation. 11, 31-45, (2013.
[4] A. a. A. B. Draa: An artificial bee colony algorithm for image contrast enhancement. Swarm and Evolutionary Computation. 16, 69-84, (2014.
[5] P. Moallem and N. Razmjooy: Optimal threshold computing in automatic image thresholding using adaptive particle swarm optimization. Journal of applied research and technology. 10, 703-712, (2012.
[6] P. Moallem, N. Razmjooy, and B. Mousavi: Robust potato color image segmentation using adaptive fuzzy inference system. Iranian Journal of Fuzzy Systems. 11, 47-65, (2014.
[7] B. S. Mousavi, P. Sargolzaei, N. Razmjooy, V. Hosseinabadi, and F. Soleymani: Digital image segmentation using rule-base classifier. American Journal of Scientific Research ISSN. 2011.
[8] B. S. Mousavi and F. Soleymani: Semantic image classification by genetic algorithm using optimised fuzzy system based on Zernike moments. Signal, Image and Video Processing. 8, 831-842, (2014.
[9] N. Razmjooy, B. S. Mousavi, B. Sadeghi, and M. Khalilpour, "Image Thresholding Optimization Based on Imperialist Competitive Algorithm," in 3rd Iranian Conference on Electrical and Electronics Engineering (ICEEE2011), 2011.
[10] N. Razmjooy, B. S. Mousavi, P. Sargolzaei, and F. Soleymani: Image thresholding based on evolutionary algorithms. International Journal of Physical Sciences. 6, 7203-7211, (2011.
[11] P. N. Suganthan: Structural pattern recognition using genetic algorithms. Pattern Recognition Letters. 35, 1883-1893, (2002.
[12] G. a. B. B. C. Garai: A novel hybrid genetic algorithm with Tabu search for optimizing multi-dimensional functions and point pattern recognition. Information Sciences. 221, 28-48, (2013.
[13] R. a. D. K. P. Malviya: Tuning of neural networks using particle swarm optimization to model MIG welding process. Swarm and Evolutionary Computation. 1, 223-235, (2011.
[14] S. J. Azadeh A, Sheikhalishahi M, Yazdani M: An integrated support vector regression–imperialist competitive algorithm for reliability estimation of a shearing machine. International Journal of Computer Integrated Manufacturing. 2015.
[15] S. Meysam Mousavi, et al.: A new support vector model-based imperialist competitive algorithm for time estimation in new product development projects. Robotics and Computer-Integrated Manufacturing. 29, 157-168, (2013.
[16] P. Moallem and N. Razmjooy: A multi layer perceptron neural network trained by invasive weed optimization for potato color image segmentation. Trends in Applied Sciences Research. 7, 445, (2012.
[17] N. Razmjooy, B. S. Mousavi, and F. Soleymani: A hybrid neural network Imperialist Competitive Algorithm for skin color segmentation. Mathematical and Computer Modelling. 57, 848-856, (2013.
[18] N. Razmjooy and M. Ramezani: Training Wavelet Neural Networks Using Hybrid Particle Swarm Optimization and Gravitational Search Algorithm for System Identification.
[19] N. Razmjooy, F. R. Sheykhahmad, and N. Ghadimi: A hybrid neural network–world cup optimization algorithm for melanoma detection. Open Medicine. 13, 9-16, (2018.
[20] J. Senthilnath, S.N. Omkar, and V. Mani: Clustering using firefly algorithm: Performance study. Swarm and Evolutionary Computation. 1, 164-171, (2011.
[21] S. J. Nanda, G. Panda: A survey on nature inspired metaheuristic algorithms for partitional clustering. Swarm and Evolutionary Computation. 16, 1-18, (2014.
[22] H. Hosseini, M. Farsadi, M. Khalilpour, and N. Razmjooy: Hybrid Energy Production System with PV Array and Wind Turbine and Pitch Angle Optimal Control by Genetic Algorithm (GA). 2011.
[23] H. Hosseini, M. Farsadi, A. Lak, H. Ghahramani, and N. Razmjooy: A Novel Method Using Imperialist Competitive Algorithm (ICA) for Controlling Pitch Angle in Hybrid Wind and PV Array Energy Production System. International Journal on Technical and Physical Problems of Engineering (IJTPE). 145-152, (
[24] H. Hosseini, B. Tousi, N. Razmjooy, and M. Khalilpour: Design robust controller for automatic generation control in restructured power system by imperialist competitive algorithm. IETE Journal of Research. 59, 745-752, (2013.
[25] N. Razmjooy and M. Khalilpour: A new design for PID controller by considering the operating points changes in Hydro-Turbine Connected to the equivalent network by using Invasive Weed Optimization (IWO) Algorithm. International Journal of Information, Security and Systems Management. 4, 468-475, (2015.
[26] N. Razmjooy and M. Khalilpour: A Robust Controller For Power System Stabilizer By Using Artificial Bee Colony Algorithm. Tech J Engin & App Sci. 5, 106-113, (2015.
[27] N. Razmjooy, M. Khalilpour, and M. Ramezani: A New Meta-Heuristic Optimization Algorithm Inspired by FIFA World Cup Competitions: Theory and Its Application in PID Designing for AVR System. Journal of Control, Automation and Electrical Systems. 27, 419-440, (2016.
[28] V. Bhargava, S.E.K. Fateen, A. Bonilla-Petriciolet: Cuckoo Search: A new nature-inspired optimization method for phase equilibrium calculations. Fluid Phase Equilibria. 337, 191-200, (2013.
[29] Y.-J. Zheng, Water wave optimization: A new nature-inspired metaheuristic. Computers & Operations Research. 55, 1-11, (2015.
[30] F.-C. Yang, Y.-P. Wang: Water flow-like algorithm for object grouping problems. Journal of the Chinese Institute of Industrial Engineers. 24, 475-488, (2007.
[31] A. Mucherino, O. Seref. Monkey search: a novel metaheuristic search for global optimization. in Data Mining. Systems Analysis and Optimization in Biomedicine. 2007.
[32] H. A. M. Abbass: Marriage in honey bees optimization-A haplometrosis polygynous swarming approach in Evolutionary Computation. Proceedings of the 2001 Congress on. 2001. IEEE. 2001.
[33] X.-S. a. S. D. Yang: Cuckoo search via Lévy flights. in Nature & Biologically Inspired Computing. NaBIC 2009. World Congress on. 2009. IEEE. 2009.
[34] R. Rajabioun: Cuckoo Optimization Algorithm. Applied Soft Computing. 11. 8, 5508-5518, (2011.
[35] X.-S. Yang: Firefly algorithms for multimodal optimization, in Stochastic algorithms: foundations and applications. Springer. 169-178, (2009.
[36] D. Simon: Biogeography-based optimization. Evolutionary Computation. IEEE Transactions on. 12, 702-713, (2008.
[37] R. C. a. J. K. Eberhart: A new optimizer using particle swarm theory. in Proceedings of the sixth international symposium on micro machine and human science. New York, NY. 1995.
[38] S.-C. Chu, P.-W. Tsai, J.-S. Pan,: Cat swarm optimization, in PRICAI 2006: Trends in Artificial Intelligence. Springer. 854-858, (2006.
[39] K. M. Passino: Biomimicry of bacterial foraging for distributed optimization and control. Control Systems. IEEE. 22, 52-67, (2002.
[40] A. H. a. A. H. A. Gandomi, Krill herd: A new bio-inspired optimization algorithm. Communications in Nonlinear Science and Numerical Simulation. 17, 4831-4845, (2012.
[41] J. H. Holland: Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. Ann Arbor, University of Michigan Press., USA. 1975.
[42] Y. Shiqin, J. Jianjun, and Y. Guangxing: A dolphin partner optimization. in Intelligent Systems. GCIS'09. WRI Global Congress on. 2009. IEEE. 2009.
[43] A. a. N. F. Kaveh: A new optimization method: Dolphin echolocation. Advances in Engineering Software. 59, 53-70, (2013.
[44] X.-S. Yang: A new metaheuristic bat-inspired algorithm, in Nature inspired cooperative strategies for optimization (NICSO 2010). Springer. 65-74, (2010.
[45] M. Dorigo: Optimization, learning and natural algorithms. 1992.
[46] F. de Lima Neto, et al. : A novel search algorithm based on fish school behavior. in Systems, Man and Cybernetics. SMC 2008. IEEE International Conference on. 2008. IEEE. 2008.
[47] J. D. Farmer, N.H. Packard, and A.S. Perelson: The immune system, adaptation, and machine learning. Physica D: Nonlinear Phenomena. Physica D: Nonlinear Phenomena. 22, 187-204, (1986.
[48] R. Tang, et al. : Wolf search algorithm with ephemeral memory. in Digital Information Management (ICDIM). 2012 Seventh International Conference on. 2012. IEEE. 2012.
[49] S. Mirjalili, S.M. Mirjalili, and A. Lewis: Grey Wolf Optimizer. Advances in Engineering Software. Advances in Engineering Software. 69, 46-61, (2014.
[50] E. Cuevas, et al.,: A swarm optimization algorithm inspired in the behavior of the social-spider. Expert Systems with Applications. 40, 6374-6384, (2013.
[51] X.-S. Yang: Flower pollination algorithm for global optimization, in Unconventional Computation and Natural Computation. Springer. 240-249, (2012.
[52] M. a. M.-R. F.-D. Ghaemi: Forest Optimization Algorithm. Expert Systems with Applications. 41, 6676-6687, (2014.
[53] H. Eskandar, et al., : Water cycle algorithm – A novel metaheuristic optimization method for solving constrained engineering optimization problems. Computers & Structures. 110–111, 151-166, (2012.
[54] M. M. a. K. E. L. Eusuff: Optimization of water distribution network design using the shuffled frog leaping algorithm. Journal of Water Resources Planning and Management. 129, 210-225, (2003.
[55] A. R. a. C. L. Mehrabian: A novel numerical optimization algorithm inspired from weed colonization. 1, 355-366, (2006.
[56] D. a. D. R. Arivudainambi: Memetic algorithm for minimum energy broadcast problem in wireless ad hoc networks. Swarm and Evolutionary Computation. 12, 57-64, (2013.
[57] J. Hofmann, S. Limmer, and D. Fey: Performance investigations of genetic algorithms on graphics cards. Swarm and Evolutionary Computation. 12, 33-47, (2013.
[58] S. A. Ludwig: Memetic algorithms applied to the optimization of workflow compositions. Swarm and Evolutionary Computation. 10, 31-40, (2013.
[59] C. Changdar, G.S. Mahapatra, and R. Kumar Pal: An efficient genetic algorithm for multi-objective solid travelling salesman problem under fuzziness. Swarm and Evolutionary Computation, . 15, 27-37, (2014.
[60] D. H. a. W. G. M. Wolpert: No free lunch theorems for optimization. Evolutionary Computation,. IEEE Transactions on. 1, 67-82, (1997.
[61] J. Liang, B. Qu, and P. Suganthan: Problem definitions and evaluation criteria for the CEC 2014 special session and competition on single objective real-parameter numerical optimization. Computational Intelligence Laboratory. 2013.
[62] E. Rashedi, H. Nezamabadi-Pour, and S. Saryazdi: GSA: a gravitational search algorithm. Information sciences. 179, 2232-2248, (2009.
[63] R. Oftadeh, M.J. Mahjoob, and M. Shariatpanahi: A novel meta-heuristic optimization algorithm inspired by group hunting of animals: Hunting search. Computers & Mathematics with Applications. 60, 2087-2098, (2010.
[64] Y.-J. Zheng: Water wave optimization: A new nature-inspired metaheuristic. Computers & Operations Research. 2014.
Published
2019-09-01
How to Cite
Arshaghi, A., Ashourian, M., & Ghabeli, L. (2019). Buzzard Optimization Algorithm: A Nature-Inspired Metaheuristic Algorithm. Majlesi Journal of Electrical Engineering, 13(3), 83-98. Retrieved from http://mjee.org/index/index.php/ee/article/view/3363
Section
Articles