GBUO: “The Good, the Bad, and the Ugly” Optimizer
Optimization problems in various fields of science and engineering should be solved using appropriate methods. Stochastic search-based optimization algorithms are a widely used approach for solving optimization problems. In this paper, a new optimization algorithm called “the good, the bad, and the...
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doaj-59d34abcf9354f88a8d98412aad83f1e2021-02-26T00:06:37ZengMDPI AGApplied Sciences2076-34172021-02-01112042204210.3390/app11052042GBUO: “The Good, the Bad, and the Ugly” OptimizerHadi Givi0Mohammad Dehghani1Zeinab Montazeri2Ruben Morales-Menendez3Ricardo A. Ramirez-Mendoza4Nima Nouri5Department of Electrical Engineering, Shahreza Campus, University of Isfahan, Isfahan, IranDepartment of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz 71557-13876, IranDepartment of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz 71557-13876, IranSchool of Engineering and Sciences, Tecnologico de Monterrey, Monterrey 64849, MexicoSchool of Engineering and Sciences, Tecnologico de Monterrey, Monterrey 64849, MexicoDepartment of Electrical Engineering, Yazd University, Yazd 89195-741, IranOptimization problems in various fields of science and engineering should be solved using appropriate methods. Stochastic search-based optimization algorithms are a widely used approach for solving optimization problems. In this paper, a new optimization algorithm called “the good, the bad, and the ugly” optimizer (GBUO) is introduced, based on the effect of three members of the population on the population updates. In the proposed GBUO, the algorithm population moves towards the good member and avoids the bad member. In the proposed algorithm, a new member called ugly member is also introduced, which plays an essential role in updating the population. In a challenging move, the ugly member leads the population to situations contrary to society’s movement. GBUO is mathematically modeled, and its equations are presented. GBUO is implemented on a set of twenty-three standard objective functions to evaluate the proposed optimizer’s performance for solving optimization problems. The mentioned standard objective functions can be classified into three groups: unimodal, multimodal with high-dimension, and multimodal with fixed dimension functions. There was a further analysis carried-out for eight well-known optimization algorithms. The simulation results show that the proposed algorithm has a good performance in solving different optimization problems models and is superior to the mentioned optimization algorithms.https://www.mdpi.com/2076-3417/11/5/2042optimizationoptimization algorithmpopulation-based algorithmexplorationexploitation |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Hadi Givi Mohammad Dehghani Zeinab Montazeri Ruben Morales-Menendez Ricardo A. Ramirez-Mendoza Nima Nouri |
spellingShingle |
Hadi Givi Mohammad Dehghani Zeinab Montazeri Ruben Morales-Menendez Ricardo A. Ramirez-Mendoza Nima Nouri GBUO: “The Good, the Bad, and the Ugly” Optimizer Applied Sciences optimization optimization algorithm population-based algorithm exploration exploitation |
author_facet |
Hadi Givi Mohammad Dehghani Zeinab Montazeri Ruben Morales-Menendez Ricardo A. Ramirez-Mendoza Nima Nouri |
author_sort |
Hadi Givi |
title |
GBUO: “The Good, the Bad, and the Ugly” Optimizer |
title_short |
GBUO: “The Good, the Bad, and the Ugly” Optimizer |
title_full |
GBUO: “The Good, the Bad, and the Ugly” Optimizer |
title_fullStr |
GBUO: “The Good, the Bad, and the Ugly” Optimizer |
title_full_unstemmed |
GBUO: “The Good, the Bad, and the Ugly” Optimizer |
title_sort |
gbuo: “the good, the bad, and the ugly” optimizer |
publisher |
MDPI AG |
series |
Applied Sciences |
issn |
2076-3417 |
publishDate |
2021-02-01 |
description |
Optimization problems in various fields of science and engineering should be solved using appropriate methods. Stochastic search-based optimization algorithms are a widely used approach for solving optimization problems. In this paper, a new optimization algorithm called “the good, the bad, and the ugly” optimizer (GBUO) is introduced, based on the effect of three members of the population on the population updates. In the proposed GBUO, the algorithm population moves towards the good member and avoids the bad member. In the proposed algorithm, a new member called ugly member is also introduced, which plays an essential role in updating the population. In a challenging move, the ugly member leads the population to situations contrary to society’s movement. GBUO is mathematically modeled, and its equations are presented. GBUO is implemented on a set of twenty-three standard objective functions to evaluate the proposed optimizer’s performance for solving optimization problems. The mentioned standard objective functions can be classified into three groups: unimodal, multimodal with high-dimension, and multimodal with fixed dimension functions. There was a further analysis carried-out for eight well-known optimization algorithms. The simulation results show that the proposed algorithm has a good performance in solving different optimization problems models and is superior to the mentioned optimization algorithms. |
topic |
optimization optimization algorithm population-based algorithm exploration exploitation |
url |
https://www.mdpi.com/2076-3417/11/5/2042 |
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