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Communication Papers of the 18th Conference on Computer Science and Intelligence Systems

Annals of Computer Science and Information Systems, Volume 37

Comparative Study: Defuzzification Functions and Their Effect on the Performance of the OFNbee Optimization Algorithm

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DOI: http://dx.doi.org/10.15439/2023F5828

Citation: Communication Papers of the 18th Conference on Computer Science and Intelligence Systems, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 37, pages 91–96 ()

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Abstract. This article explores the pivotal role of defuzzification functions in the operation of the OFNBee algorithm, which employs ordered fuzzy number arithmetic to harness the inherent dynamics within a hive. Defuzzification functions serve the purpose of representing the OFN (Ordered Fuzzy Number) as a real number, while fuzzification functions convert real numbers into OFN representations. By focusing on the defuzzification function, this study investigates its impact on the performance of the OFNBee algorithm. The research demonstrates that tailoring dedicated fuzzification functions for specific optimization problems can yield substantial improvements in algorithmic performance. It is important to note that the overall performance of the algorithm relies on both the fuzzification and defuzzification functions. Consequently, this article provides valuable insights into the effects of the defuzzification function on algorithmic outcomes.

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