An elitist teaching-learning-based optimization algorithm for solving complex constrained optimization problems
By R. Venkata Rao, Vivek K. Patel. 2012
550 citations recorded by the source corpus.
A B S T R A C T Nature inspired population based algorithms is a research field which simulates different natural phenomena to solve a wide range of problems. Researchers have proposed several algorithms considering different natural phenomena. Teaching-Learning-based optimization (TLBO) is one of the recently proposed population based algorithms which simulates the teaching-learning process of the class room. This algorithm does not require any algorithm-specific control parameters. In this paper, elitism concept is introduced in the TLBO algorithm and its effect on the performance of the algorithm is investigated. The effects of common controlling parameters such as the population size and the number of generations on the performance of the algorithm are also investigated. The proposed algorithm is tested on 35 constrained benchmark functions with different characteristics and the performance of the algorithm is compared with that of other well known optimization algorithms. The proposed algorithm can be applied to various optimization problems of the industrial environment.
Subfields: Artificial Intelligence
View Paper: https://www.semanticscholar.org/paper/230bf81fc9ce667f9a846cbfc1c28e0ae0e8df42
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In Artificial Intelligence
- Tansel Dökeroğlu
- Vivek D. Kalyankar
- Ahmet Cosar
- Tayfun Kucukyilmaz
- Ender Sevinç
- Ayça Deniz
- Hakan Ezgi Kiziloz
- R. Venkata Rao
Citations in the corpus
- Parameter optimization of modern machining processes using teaching-learning-based optimization algorithm (2013, background): “Rao and Patel [39] described the TLBO algorithm for solving complex constrained optimization problems.”
- A survey on new generation metaheuristic algorithms (2019, methodology): “This new method has been applied to engineering design optimization problems (Rao et al., 2012; Rao and Patel, 2012).”
- A comprehensive survey on recent metaheuristics for feature selection (2022, background): “The algorithm has gained attention with respect to its rapid convergence and the lack of algorithmic parameters [201, 202].”
- Jaya: A simple and new optimization algorithm for solving constrained and unconstrained optimization problems (2016, unknown): “Some of them are: Harmony Search (HS) algorithm, Gravitational Search Algorithm (GSA), Biogeography-Based Optimization (BBO), Grenade Explosion Method (GEM), etc. (Rao and Patel, 2012, 2013).”