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.
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Cites
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GSA: A Gravitational Search Algorithm
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A New Heuristic Optimization Algorithm: Harmony Search
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AN IDEA BASED ON HONEY BEE SWARM FOR NUMERICAL OPTIMIZATION
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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
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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
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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