Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.13091/5240
Title: Bindmo: a New Binary Dwarf Mongoose Optimization Algorithm on Based Z-Shaped, U-Shaped, and Taper-Shaped Transfer Functions for Cec-2017 Benchmarks
Authors: Baş, Emine
Keywords: CEC-2017
DMO
Dwarf mongoose
Transfer functions
Heuristic algorithms
Population statistics
Transfer functions
Binary optimization
CEC-2017
Dwarf mongoose
Dwarf mongoose optimization
Heuristics algorithm
Intelligent swarm
Optimisations
Optimization algorithms
Real-world problem
U-shaped
Optimization
Publisher: Springer Science and Business Media Deutschland GmbH
Abstract: Intelligent swarm optimization algorithms have become increasingly common due to their success in solving real-world problems. Dwarf Mongoose Optimization (DMO) algorithm is a newly proposed intelligent swarm optimization algorithm in recent years. It was developed for continuous optimization problem solutions in its original paper. But real-world problems are not always problems that take continuously variable values. Real-world problems are often problems with discrete variables. Therefore, heuristic algorithms proposed for continuous optimization problems need to be updated to solve discrete optimization problems. In this study, DMO has been updated for binary optimization problems and the Binary DMO (BinDMO) algorithm has been proposed. In binary optimization, the search space consists of binary variable values. Transfer functions are often used in the conversion of continuous variable values to binary variable values. In this study, twelve different transfer functions were used (four Z-shaped, four U-shaped, and four Taper-shaped). Thus, twelve different BinDMO variations were obtained (BinDMO1, BinDMO2, …, BinDMO12). The achievements of BinDMO variations were tested on thirteen different unimodal and multimodal classical benchmark functions. The effectiveness of population sizes on the effectiveness of BinDMO was also investigated. When the results were examined, it was determined that the most successful BinDMO variation was BinDMO1 (with Z1-shaped transfer function). The most successful BinDMO variation was compared with three different binary heuristic algorithms selected from the literature (SO, PDO, and AFT) on CEC-2017 benchmark functions. According to the average results, BinDMO was the most successful binary heuristic algorithm. This has proven that BinDMO can be chosen as an alternative algorithm for binary optimization problems. © The Author(s) 2024.
URI: https://doi.org/10.1007/s00521-024-09436-0
https://hdl.handle.net/20.500.13091/5240
ISSN: 0941-0643
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collections

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