پیوندهای مفید
آمار
| تعداد نشریات | 7 |
| تعداد شماره‌ها | 405 |
| تعداد مقالات | 5,424 |
| تعداد مشاهده مقاله | 5,539,209 |
| تعداد دریافت فایل اصل مقاله | 5,025,540 |
Optimal Placement of Capacitor Banks Using a New Modified Version of Teaching-Learning- Based Optimization Algorithm | ||
| AUT Journal of Modeling and Simulation | ||
| مقاله 9، دوره 50، شماره 2، اسفند 2018، صفحه 171-180 اصل مقاله (680.49 K) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22060/miscj.2018.14594.5111 | ||
| نویسندگان | ||
| A. Rahiminejad1؛ A. Foroughi Nematollahi2؛ B. Vahidi* 2؛ S. Shahrooyan3 | ||
| 1Department of Electrical and Computer Science, Esfarayen University of Technology, Esfarayen, Iran | ||
| 2Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran | ||
| 3Department of English Literature, Hakim Sabzevari University, Sabzevar, Iran | ||
| چکیده | ||
| Meta-heuristics optimization methods are important techniques for optimal design of the engineering systems. Numerous Meta-heuristics methods, all inspired by different nature phenomena, have been introduced in the literature. A new modified version of Teaching-Learning-Based Optimization (TLBO) Algorithm is introduced in this paper. TLBO, as a parameter-free algorithm, is based on the learning procedure of students in a classroom. In the Conventional TLBO (CTLBO), the students enhance their grade in two phases known as teacher phase and student phase. In the former, the teacher tries to enhance the average of the class. In the latter, the students share their knowledge in the groups of two. In the proposed Modified TLBO (MTLBO), the students participate in the groups of several students and improve their knowledge based on the grade of these students. Participating in the meeting with more than two students increases the probability of enhancing the student marks. To testify the performance of the proposed algorithm, it is applied to the problem of optimal capacitor placement with the aim of annual net saving maximization and system stability enhancement. The test systems are 34-bus and 94- bus radial test systems. The comparison of the results with those from off-the-shelf algorithms clears the appropriate performance, fast convergence, and superiority of the proposed algorithm. | ||
| کلیدواژه‌ها | ||
| Teaching-Learning-Based Optimization algorithm؛ capacitor placement؛ net saving maximization؛ system stability enhancement | ||
| مراجع | ||
|
[1] H. Holland John, Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence, USA: University of Michigan, (1975). [2] R. Storn, K. Price, Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces, Journal of global optimization, 11(4) (1997) 341-359.
[3] J.D. Farmer, N.H. Packard, A.S. Perelson, The immune system, adaptation, and machine learning, Physica D: Nonlinear Phenomena, 22(1-3) (1986) 187-204.
[4] K.M. Passino, Biomimicry of bacterial foraging for distributed optimization and control, IEEE control systems, 22(3) (2002) 52-67. [5] J. Kennedy, R. Eberhart, PSO optimization, in: Proc. IEEE Int. Conf. Neural Networks, IEEE Service Center, Piscataway, NJ, 1995, pp. 1941-1948. [6] M. Dorigo, V. Maniezzo, A. Colorni, Positive feedback as a search strategy. Dipartimento di Elettronica, Politecnico di Milano, Italy, Tech. Rep. 91-016, 1991. [7] M.M. Eusuff, K.E. Lansey, Optimization of water distribution network design using the shuffled frog leaping algorithm, Journal of Water Resources planning and management, 129(3) (2003) 210-225. [8] D. Karaboga, An idea based on honey bee swarm for numerical optimization, Technical report-tr06, Erciyes university, engineering faculty, computer engineering department, 2005. [9] A.F. Nematollahi, A. Rahiminejad, B.J.A.S.C. Vahidi, A novel physical based meta-heuristic optimization method known as lightning attachment procedure optimization, 59 (2017) 596-621. [10] T. Back, Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms, Oxford university press, 1996. [11] R.V. Rao, V. Savsani, J. Balic, Teaching–learning-based optimization algorithm for unconstrained and constrained real-parameter optimization problems, Engineering Optimization, 44(12) (2012) 1447-1462. [12] R. Venkata Rao, V. Patel, Multi-objective optimization of combined Brayton and inverse Brayton cycles using advanced optimization algorithms, Engineering Optimization, 44(8) (2012) 965-983. [13] S.C. Satapathy, A. Naik, Data clustering based on teaching-learning-based optimization, in: International Conference on Swarm, Evolutionary, and Memetic Computing, Springer, 2011, pp. 148-156. [14] V. Toğan, Design of planar steel frames using teaching–learning based optimization, Engineering Structures, 34 (2012) 225-232. [15] R. Venkata Rao, V. Kalyankar, Parameter optimization of machining processes using a new optimization algorithm, Materials and Manufacturing Processes, 27(9) (2012) 978-985. [16] R.V. Rao, V.J. Savsani, D. Vakharia, Teaching–learning-based optimization: an optimization method for continuous non-linear large scale problems, Information Sciences, 183(1) (2012) 1-15. [17] S.C. Satapathy, A. Naik, K. Parvathi, Unsupervised feature selection using rough set and teaching learning-based optimisation, International Journal of Artificial Intelligence and Soft Computing, 3(3) (2013) 244-256. [18] S.C. Satapathy, A. Naik, K. Parvathi, Unsupervised feature selection using rough set and teaching learning-based optimisation, International Journal of Artificial Intelligence and Soft Computing, 3(3) (2013) 244-256. [19] S.C. Satapathy, A. Naik, K. Parvathi, Weighted teaching-learning-based optimization for global function optimization, Applied Mathematics, 4(03) (2013) 429. [20] S.C. Satapathy, A. Naik, K. Parvathi, A teaching learning based optimization based on orthogonal design for solving global optimization problems, SpringerPlus, 2(1) (2013) 130. [21] D. Chen, F. Zou, Z. Li, J. Wang, S. Li, An improved teaching–learning-based optimization algorithm for solving global optimization problem, Information Sciences, 297 (2015) 171-190. [22] S.C. Satapathy, A. Naik, Modified Teaching–Learning-Based Optimization algorithm for global numerical optimization—A comparative study, Swarm and Evolutionary Computation, 16 (2014) 28-37. [23] R.V. Rao, V. Patel, An improved teaching-learning-based optimization algorithm for solving unconstrained optimization problems, Scientia Iranica, 20(3) (2013) 710-720. [24] B. Mandal, P.K. Roy, Optimal reactive power dispatch using quasi-oppositional teaching learning based optimization, International Journal of Electrical Power & Energy Systems, 53 (2013) 123-134. [25] N. Khalesi, N. Rezaei, M.-R. Haghifam, DG allocation with application of dynamic programming for loss reduction and reliability improvement, International Journal of Electrical Power & Energy Systems, 33(2) (2011) 288-295. [25] N. Khalesi, N. Rezaei, M.-R. Haghifam, DG allocation with application of dynamic programming for loss reduction and reliability improvement, International Journal of Electrical Power & Energy Systems, 33(2) (2011) 288-295. [27] A. Forooghi Nematollahi, A. Dadkhah, O. Asgari Gashteroodkhani, B.J.J.o.R. Vahidi, S. Energy, Optimal sizing and siting of DGs for loss reduction using an iterative-analytical method, 8(5) (2016) 055301. [28] A. Foroughi Nematollahi, A. Rahiminejad, B. Vahidi, H. Askarian, A.J.J.o.R. Safaei, S. Energy, A new evolutionary-analytical two-step optimization method for optimal wind turbine allocation considering maximum capacity, 10(4) (2018) 043312. [29] M. Hamzeh, B. Vahidi, A.F.J.I.T.o.I.I. Nematollahi, Optimizing Configuration of Cyber Network Considering Graph Theory Structure and Teaching-Learning-Based Optimization (GT-TLBO), (2018). [30] K. Moosavi, B. Vahidi, H. Askarian Abyaneh, A.J.J.o.R. Foroughi Nematollahi, S. Energy, Intelligent control of power sharing between parallel-connected boost converters in micro-girds, 9(6) (2017) 065504. [31] G.I. Rashed, Y. Sun, H. Shaheen, Optimal TCSC placement in a power system by means of Differential Evolution Algorithm considering loss minimization, in: Industrial Electronics and Applications (ICIEA), 2011 6th IEEE Conference on, IEEE, 2011, pp. 2209-2215. [32] R.S. Rao, K. Ravindra, K. Satish, S. Narasimham, Power loss minimization in distribution system using network reconfiguration in the presence of distributed generation, IEEE transactions on power systems, 28(1). | ||
|
آمار تعداد مشاهده مقاله: 21,775 تعداد دریافت فایل اصل مقاله: 538 |
||