The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.
An improved teaching-learning-based optimization algorithm for solving unconstrained optimization problems
Open accessAbstract
Teaching–Learning-Based Optimization (TLBO) algorithms simulate the teaching–learning phenomenon of a classroom to solve multi-dimensional, linear and nonlinear problems with appreciable efficiency. In this paper, the basic TLBO algorithm is improved to enhance its exploration and exploitation capacities by introducing the concept of number of teachers, adaptive teaching factor, tutorial training and self motivated learning. Performance of the improved TLBO algorithm is assessed by implementing it on a range of standard unconstrained benchmark functions having different characteristics. The results of optimization obtained using the improved TLBO algorithm are validated by comparing them with those obtained using the basic TLBO and other optimization algorithms available in the literature.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Kennedy, J., & Eberhart, R. (2002) · Proceedings of ICNN'95 - International Conference on Neural Networks · doi:10.1109/icnn.1995.488968 · Cited by 58,192
Holland, J. H. (1992) · The MIT Press eBooks · doi:10.7551/mitpress/1090.001.0001 · Cited by 47,002
Storn, R., & Price, K. (1997) · Journal of Global Optimization · doi:10.1023/a:1008202821328 · Cited by 29,569
Karaboga, D., & Basturk, B. (2007) · Journal of Global Optimization · doi:10.1007/s10898-007-9149-x · Cited by 7,651
GSA: A Gravitational Search Algorithm
Rashedi, E., Nezamabadi-pour, H., & Saryazdi, S. (2009) · Information Sciences · doi:10.1016/j.ins.2009.03.004 · Cited by 7,358
A New Heuristic Optimization Algorithm: Harmony Search
Geem, Z. W., Kim, J. H., & Loganathan, G. V. (2001) · SIMULATION · doi:10.1177/003754970107600201 · Cited by 6,247
AN IDEA BASED ON HONEY BEE SWARM FOR NUMERICAL OPTIMIZATION
Karaboğa, D. (2005) · Cited by 5,466
Rao, R. V., Savsani, V. J., & Vakharia, D. P. (2011) · Computer-Aided Design · doi:10.1016/j.cad.2010.12.015 · Cited by 5,150
Self-citationBiogeography-Based Optimization
Simon, D. (2008) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2008.919004 · Cited by 4,305
On the performance of artificial bee colony (ABC) algorithm
Karaboga, D., & Basturk, B. (2008) · Applied Soft Computing · doi:10.1016/j.asoc.2007.05.007 · Cited by 3,672
A comparative study of Artificial Bee Colony algorithm
Karaboga, D., & Akay, B. (2009) · Applied Mathematics and Computation · doi:10.1016/j.amc.2009.03.090 · Cited by 3,206
Biomimicry of bacterial foraging for distributed optimization and control
(2002) · IEEE Control Systems · doi:10.1109/mcs.2002.1004010 · Cited by 3,069
Artificial Intelligence through Simulated Evolution
Fogel, L. J., Owens, A. J., & Walsh, M. J. (1966) · International Linear Collider · Cited by 2,796
An improved harmony search algorithm for solving optimization problems
Mahdavi, M., Fesanghary, M., & Damangir, E. (2007) · Applied Mathematics and Computation · doi:10.1016/j.amc.2006.11.033 · Cited by 1,863
Stochastic ranking for constrained evolutionary optimization
Runarsson, T. P., & Yao, X. (2000) · IEEE Transactions on Evolutionary Computation · doi:10.1109/4235.873238 · Cited by 1,807
Rao, R. V., Savsani, V. J., & Vakharia, D. P. (2012) · Information Sciences · doi:10.1016/j.ins.2011.08.006 · Cited by 1,792
Self-citationOptimization of Water Distribution Network Design Using the Shuffled Frog Leaping Algorithm
Eusuff, M. M., & Lansey, K. E. (2003) · Journal of Water Resources Planning and Management · doi:10.1061/(asce)0733-9496(2003)129:3(210 · Cited by 1,390
A novel heuristic optimization method: charged system search
Kaveh, A., & Talatahari, S. (2010) · Acta Mechanica · doi:10.1007/s00707-009-0270-4 · Cited by 1,354
Gbest-guided artificial bee colony algorithm for numerical function optimization
Zhu, G., & Kwong, S. (2010) · Applied Mathematics and Computation · doi:10.1016/j.amc.2010.08.049 · Cited by 1,179
A modified Artificial Bee Colony algorithm for real-parameter optimization
Akay, B., & Karaboğa, D. (2010) · Information Sciences · doi:10.1016/j.ins.2010.07.015 · Cited by 1,174
The immune system, adaptation, and machine learning
Farmer, J. D., Packard, N. H., & Perelson, A. S. (1986) · Physica D: Nonlinear Phenomena · doi:10.1016/0167-2789(86)90240-x · Cited by 1,087
The decay of turbulence in the burgers model
Moiseev, S. S., Toor, A. V., & Yanovsky, V. V. (1981) · Physica D: Nonlinear Phenomena · doi:10.1016/0167-2789(81)90072-5 · Cited by 1,045
Rao, R. V., & Patel, V. K. (2012) · International Journal of Industrial Engineering Computations · doi:10.5267/j.ijiec.2012.03.007 · Cited by 511
Self-citationA hybrid simplex search and particle swarm optimization for unconstrained optimization
Fan, S.-K. S., & Zahara, E. (2007) · European Journal of Operational Research · doi:10.1016/j.ejor.2006.06.034 · Cited by 350
A note on teaching–learning-based optimization algorithm
Črepinšek, M., Liu, S.-H., & Mernik, L. (2012) · Information Sciences · doi:10.1016/j.ins.2012.05.009 · Cited by 296
Li, G., Niu, P., & Xiao, X. (2012) · Applied Soft Computing · doi:10.1016/j.asoc.2011.08.040 · Cited by 273
(2005) · Natural Computing Series · doi:10.1007/3-540-31306-0 · Cited by 256
Noel, M. M. (2012) · Applied Soft Computing · doi:10.1016/j.asoc.2011.08.037 · Cited by 186
A clustering-based differential evolution for global optimization
Cai, Z., Gong, W., Ling, C. X., & Zhang, H. (2010) · Applied Soft Computing · doi:10.1016/j.asoc.2010.04.008 · Cited by 143
Kaveh, A., & Talatahari, S. (2010) · Structural and Multidisciplinary Optimization · doi:10.1007/s00158-010-0571-1 · Cited by 87
Hybrid optimization with improved tabu search
Mashinchi, M. H., Orgun, M. A., & Witold Pedrycz, C. (2010) · Applied Soft Computing · doi:10.1016/j.asoc.2010.06.015 · Cited by 52
Liu, G., Li, Y., Nie, X., et al. (2011) · Applied Soft Computing · doi:10.1016/j.asoc.2011.09.020 · Cited by 41
A new hybrid method for solving global optimization problem
Moumen, S. E., Ellaia, R., & Aboulaïch, R. (2011) · Applied Mathematics and Computation · doi:10.1016/j.amc.2011.08.066 · Cited by 18
Cited by
Shareef, H., Ibrahim, A. A., & Mutlag, A. H. (2015) · Applied Soft Computing · doi:10.1016/j.asoc.2015.07.028 · Cited by 422
Metadata
- Authors
- Rao, R. V., & Patel, V.
- Venue
- Scientia Iranica
- Publisher
- Elsevier BV
- Published
- 2012-12
- ISSN
- 1026-3098 (ISSN-L)
- OpenAlex
- W2066650517
- Cited by
- 384
- References
- 33
- FWCI
- 19.63
- Type
- journal article
- Language
- en
- Open access
- diamond
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
