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Neural Computing and Applications

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14.07.2016 | Original Article

Fuzzy adaptive teaching–learning-based optimization for global numerical optimization

Erschienen in: Neural Computing and Applications | Ausgabe 2/2018

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Abstract

Teaching–learning-based optimization (TLBO) is one of the latest metaheuristic algorithms being used to solve global optimization problems over continuous search space. Researchers have proposed few variants of TLBO to improve the performance of the basic TLBO algorithm. This paper presents a new variant of TLBO called fuzzy adaptive teaching–learning-based optimization (FATLBO) for numerical global optimization. We propose three new modifications to the basic scheme of TLBO in order to improve its searching capability. These modifications consist, namely of a status monitor, fuzzy adaptive teaching–learning strategies, and a remedial operator. The performance of FATLBO is investigated on four experimental sets comprising complex benchmark functions in various dimensions and compared with well-known optimization methods. Based on the results, we conclude that FATLBO is able to deliver excellence and competitive performance for global optimization.

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Titel: Fuzzy adaptive teaching–learning-based optimization for global numerical optimization
Publikationsdatum: 14.07.2016
Erschienen in: Neural Computing and Applications / Ausgabe 2/2018
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-016-2449-7

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