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## FOA: ‘Following’ Optimization Algorithm for solving Power engineering optimization problems | ||

Journal of Operation and Automation in Power Engineering | ||

مقاله 17، دوره 8، شماره 1، اردیبهشت 2020، صفحه 57-64 اصل مقاله (919.03 K)
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نوع مقاله: Research paper | ||

شناسه دیجیتال (DOI): 10.22098/joape.2019.5522.1414 | ||

نویسندگان | ||

M. Dehghani^{*} ^{1}؛ M. Mardaneh^{1}؛ O. P. Malik^{2}
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^{1}Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz, Iran. | ||

^{2}Department of Electrical Engineering, University of Calgary, Calgary Alberta Canada. | ||

چکیده | ||

These days randomized-based population optimization algorithms are in wide use in different branches of science such as bioinformatics, chemical physics andpower engineering. An important group of these algorithms is inspired by physical processes or entities’ behavior. A new approach of applying optimization-based social relationships among the members of a community is investigated in this paper. In the proposed algorithm, search factors are indeed members of the community who try to improve the community by ‘following’ each other. FOA implemented on 23 well-known benchmark test functions. It is compared with eight optimization algorithms. The paper also considers for solving optimal placement of Distributed Generation (DG). The obtained results show that FOA is able to provide better results as compared to the other well-known optimization algorithms. | ||

کلیدواژهها | ||

optimization؛ social relationships؛ heuristic algorithms؛ following optimization, following | ||

مراجع | ||

[1] M. Dehghani, Z. Montazeri, A. Dehghani, N. Nouri, and A. Seifi, "BSSA: Binary spring search algorithm," [2] M. Dehghani, Z. Montazeri, A. Dehghani, and A. Seifi, "Spring search algorithm: A new meta-heuristic optimization algorithm inspired by Hooke's law," [3] M. Dehghani, Z. Montazeri, O. P. Malik, A. Ehsanifar, and A. Dehghani, "OSA: Orientation Search Algorithm," [4] M. Dehghani, M. Mardaneh, O. P. Malik, and S. M. NouraeiPour, "DTO: Donkey Theorem Optimization," [5] M. Bielli and P. Carotenuto, "Genetic algorithms and transportation analysis: review and perspectives for bus network optimization," [6] I. V. Antonov, E. Mazurov, M. Borodovsky, and Y. A. Medvedeva, "Prediction of lncRNAs and their interactions with nucleic acids: benchmarking bioinformatics tools," [7] Z. Montazeri and T. Niknam, "Energy carriers management based on energy consumption," [8] S. Darby, T. V. Mortimer-Jones, R. L. Johnston, and C. Roberts, "Theoretical study of Cu–Au nanoalloy clusters using a genetic algorithm," [9] S. Dehbozorgi, A. Ehsanifar, Z. Montazeri, M. Dehghani, and A. Seifi, "Line loss reduction and voltage profile improvement in radial distribution networks using battery energy storage system," [10] C. A. C. Coello, E. H. Luna, and A. H. Aguirre, "Use of particle swarm optimization to design combinational logic circuits," [11] K.-S. Tang, K.-F. Man, S. Kwong, and Q. He, "Genetic algorithms and their applications," [12] J. D. Farmer, N. H. Packard, and A. S. Perelson, "The immune system, adaptation, and machine learning," [13] M. Dorigo and L. M. Gambardella, "Ant colony system: a cooperative learning approach to the traveling salesman problem," [14] J. Kenny, "Particle swarm optimization," [15] K. E. Voges, N. Pope, and M. R. Brown, "Cluster analysis of marketing data examining on-line shopping orientation: A comparison of k-means and rough clustering approaches," [16] A. Lazar, "Heuristic knowledge discovery for archaeological data using genetic algorithms and rough sets," [17] G. Gigerenzer and W. Gaissmaier, "Heuristic decision making," [18] S. Kirkpatrick, C. D. Gelatt, and M. P. Vecchi, "Optimization by simulated annealing," [19] Z. W. Geem, J. H. Kim, and G. Loganathan, "A new heuristic optimization algorithm: harmony search," [20] S. A. Hofmeyr and S. Forrest, "Architecture for an artificial immune system," [21] M. Dorigo and T. Stützle, "Ant colony optimization: overview and recent advances," [22] K. M. Passino, "Biomimicry of bacterial foraging for distributed optimization and control," [23] A. Silva, A. Neves, and E. Costa, "An empirical comparison of particle swarm and predator prey optimisation," [24] R. A. Formato, "Central force optimization: a new metaheuristic with applications in applied electromagnetics," [25] P. Tarasewich and P. R. McMullen, "Swarm intelligence: power in numbers," [26] A. E. Eiben and C. A. Schippers, "On evolutionary exploration and exploitation," [27] X. Yao, Y. Liu, and G. Lin, "Evolutionary programming made faster," [28] E. Rashedi, H. Nezamabadi-Pour, and S. Saryazdi, "GSA: a gravitational search algorithm," [29] P. Sarzaeim, O. Bozorg-Haddad, and X. Chu, "Teaching-Learning-Based Optimization (TLBO) Algorithm," [30] S. Mirjalili, S. M. Mirjalili, and A. Lewis, "Grey wolf optimizer," [31] S. Mirjalili and A. Lewis, "The whale optimization algorithm," [32] S. Saremi, S. Mirjalili, and A. Lewis, "Grasshopper optimisation algorithm: theory and application," [33] G. Dhiman and V. Kumar, "Emperor penguin optimizer: A bio-inspired algorithm for engineering problems," [34] M. Dehghani, M. Mardaneh, Z. Montazeri, A. Ehsanifar, M. J. Ebadi, and O. M. Grechko, "Spring search algorithm for simultaneous placement of distributed generation and capacitors," [35] R. Afshan and J. Salehi, "Optimal scheduling of battery energy storage system in distribution network considering uncertainties using hybrid Monte Carlo-Genetic approach," [36] Z. Luburić and H. Pandžić, "FACTS devices and energy storage in unit commitment," [37] K. Mazlumi and A. Shabani, "DC microgrid protection in the presence of the photovoltaic and energy storage systems," [38] A. Ehsanifar, M. Dehghani, and M. Allahbakhshi, "Calculating the leakage inductance for transformer inter-turn fault detection using finite element method," [39] M. Dehghani, Z. Montazeri, A. Ehsanifar, A. R. Seifi, M. J. Ebadi, and O. M. Grechko, "Planning of energy carriers based on final energy consumption using dynamic programming and particle swarm optimization," [40] Z. Montazeri and T. Niknam, "Optimal utilization of electrical energy from power plants based on final energy consumption using gravitational search algorithm," [41] P. A. Daly and J. Morrison, "Understanding the potential benefits of distributed generation on power delivery systems," [42] T. Gözel and M. H. Hocaoglu, "An analytical method for the sizing and siting of distributed generators in radial systems," [43] C. Wang and M. H. Nehrir, "Analytical approaches for optimal placement of distributed generation sources in power systems," [44] M. Ettehadi, H. Ghasemi, and S. Vaez-Zadeh, "Voltage stability-based DG placement in distribution networks," | ||

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