A comprehensive investigation into the performance of optimization methods in spur gear design

Atila, Ümit; Dörterler, Murat; Durgut, Rafet; Şahin, İsmail

doi:10.1080/0305215x.2019.1634702

Cited by 13 publications

(4 citation statements)

References 27 publications

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“…25 Pack members take on one of four different roles in the GWO method. 23 These roles are defined as alpha (α), Beta (β), delta (δ) and omega (ω) from top to bottom hierarchically. Each solution in the population corresponds to one wolf in the herd.…”

Section: Optimizers Applied To the Problemmentioning

confidence: 99%

“…28–30 Each food source represents a corresponding solution in the search space, and the amount of nutrients represents the fitness value of the solution. 23,31 The pseudocode of ABC is given in Algorithm 2. Where the maximum number of trials of a solution is set as 100 in this study.…”

Section: Optimizers Applied To the Problemmentioning

confidence: 99%

“…Heuristic methods, which have better search capability than conventional methods, have been widely used since Atila et al 23 created a new paradigm in design optimization of gears with the GA approach for gear optimization with minimum weight. GA, one of the population-based heuristics, is widely used in the design optimization of planetary gears.…”

Section: Introductionmentioning

confidence: 99%

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Optimization of planetary gearbox using nature inspired meta-heuristic optimizers

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Dorterler,

Sahin

2023

Proceedings of the Institution of Mechanical Engineers, Part C:

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In this study, a detailed research was carried out to solve the two-stage planetary gearbox design optimization problem by using nature-inspired optimization methods. Seven different nature-inspired meta-heuristic optimizers were applied to minimize the volume of the planetary gear. The recent optimizers, Grey Wolf Optimizer, Artificial Bee Colony, Multi-verse Optimizer, Cuckoo Search, Whale Optimization Algorithm and Salp Swarm Algorithm methods were applied to the problem for the first time. The optimal volume values obtained with the seven different optimizers are much better than the best values known in the literature. The best fitness value obtained is 30% better than the best known in the literature.

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Section: Optimizers Applied To the Problemmentioning

confidence: 99%

Section: Optimizers Applied To the Problemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optimization of planetary gearbox using nature inspired meta-heuristic optimizers

Top,

Dorterler,

Sahin

2023

Proceedings of the Institution of Mechanical Engineers, Part C:

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“…In computer science, many optimization algorithms have been developed by inspiring with living creatures in nature to find out the optimum solution among all feasible solutions. The nature-inspired optimization algorithms are being used in numerous research areas such as computer science [30,31], data mining [32,33], industry [34], agriculture [35], medicine [36], economy [37], and engineering [38]. GWO is a comparably novel nature-inspired optimization approach.…”

Section: Introductionmentioning

confidence: 99%

The Adaptation of Gray Wolf Optimizer to Data Clustering

Tekerek

Dörterler

2022

Politeknik Dergisi

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Data Clustering stands for a group of methods classifying patterns into groups and retrieving similarities or dissimilarities of a collection of objects. Clustering is used for pattern recognition, machine learning, etc. One of the approaches to clustering is optimization. The aim of the optimization is finding the best solution in the search space of a problem as much as possible. Many optimization methods were modified to solve clustering problems in literature. Gray Wolf Optimizer (GWO) is one of the nature-inspired meta-heuristic algorithms simulating the hunting of gray wolves. GWO has applied to solve several optimization issues in different fields. In this study, GWO was examined in the case of data clustering. GWO was modified to get better clustering results and applied to well-known benchmark data sets. The performance of GWO was compared to the other algorithms used as clustering. The results show that GWO can be used for data clustering successfully.

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