Grey wolf optimizer (GWO) for automated offshore crane design

Hameed, Ibrahim A.; Bye, Robin Trulssen; Osen, Ottar L.

doi:10.1109/ssci.2016.7849998

Cited by 23 publications

(6 citation statements)

References 10 publications

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“…5. It is the capacity to avoid local minima and govern algorithm performance with only two control parameters, resulting in increased stability and robustness [58].…”

Section: Grey Wolf Optimizer (Gwo) Methodsmentioning

confidence: 99%

A literature Survey on Well-known Algorithms of Optimum Power Flow

Al-lamey¹,

Kshash²,

Warid³

2022

Proceedings of 2nd International Multi-Disciplinary Conference Theme: Integrated Sciences and Technologies, IMDC-IST 2021, 7-9

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One of the most often researched non-linear optimization issues is the optimal power flow (OPF) problem. The performance of electricity production, transmission, and distribution must be improved under a variety of system needs and control restrictions in general (OPF). The compositions of OPFs and the methods used to apply them are quite diverse. Furthermore, as a result of recent power markets and the absorption of renewable resources, the character of OPF is evolving. Various OPF formulations have been dealt with using a variety of classical and meta-heuristic methods throughout the last three decades. This survey explores both traditional and intelligent OPF approaches to provide a strong background for the present status of OPF formulations and solutions approaches. The survey covers all of the optimization approaches that researchers have used to (OPF), with a focus on the advantages and disadvantages of each method for both traditional and intelligent OPF methods.

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“…5. It is the capacity to avoid local minima and govern algorithm performance with only two control parameters, resulting in increased stability and robustness [58].…”

Section: Grey Wolf Optimizer (Gwo) Methodsmentioning

confidence: 99%

A literature Survey on Well-known Algorithms of Optimum Power Flow

Al-lamey¹,

Kshash²,

Warid³

2022

Proceedings of 2nd International Multi-Disciplinary Conference Theme: Integrated Sciences and Technologies, IMDC-IST 2021, 7-9

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“…It has a simple structure, which allows for fewer computational requirements. It also reduces its search space increasing its convergence speed compared to other algorithms [37].…”

Section: Grey Wolf Optimizermentioning

confidence: 99%

Metaheuristic Parameter Identification of Motors Using Dynamic Response Relations

Rodríguez-Abreo

Rodríguez‐Reséndiz

Álvarez-Alvarado

et al. 2022

Sensors

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This article presents the use of the equations of the dynamic response to a step input in metaheuristic algorithm for the parametric estimation of a motor model. The model equations are analyzed, and the relations in steady-state and transient-state are used as delimiters in the search. These relations reduce the number of random parameters in algorithm search and reduce the iterations to find an acceptable result. The tests were implemented in two motors of known parameters to estimate the performance of the modifications in the algorithms. Tests were carried out with three algorithms (Gray Wolf Optimizer, Jaya Algorithm, and Cuckoo Search Algorithm) to prove that the benefits can be extended to various metaheuristics. The search parameters were also varied, and tests were developed with different iterations and populations. The results show an improvement for all the algorithms used, achieving the same error as the original method but with 10 to 50% fewer iterations.

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“…The GWO algorithm includes two main control parameters,

and C , and local search random vector parameters

and

from

. GWO has a few important features such as faster convergence due to continuous reduction of the search space and fewer decision variables (i.e.,

, and

, avoiding local minima, better stability, and robustness [ 44 ]. Comparing other standard SI algorithms, GWO performs well in robot swarm learning [ 13 ].…”

Section: Algorithms For Text Document Clusteringmentioning

confidence: 99%

Swarm Intelligence Algorithms in Text Document Clustering with Various Benchmarks

Selvaraj

Choi

2021

Sensors

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Text document clustering refers to the unsupervised classification of textual documents into clusters based on content similarity and can be applied in applications such as search optimization and extracting hidden information from data generated by IoT sensors. Swarm intelligence (SI) algorithms use stochastic and heuristic principles that include simple and unintelligent individuals that follow some simple rules to accomplish very complex tasks. By mapping features of problems to parameters of SI algorithms, SI algorithms can achieve solutions in a flexible, robust, decentralized, and self-organized manner. Compared to traditional clustering algorithms, these solving mechanisms make swarm algorithms suitable for resolving complex document clustering problems. However, each SI algorithm shows a different performance based on its own strengths and weaknesses. In this paper, to find the best performing SI algorithm in text document clustering, we performed a comparative study for the PSO, bat, grey wolf optimization (GWO), and K-means algorithms using six data sets of various sizes, which were created from BBC Sport news and 20 newsgroups. Based on our experimental results, we discuss the features of a document clustering problem with the nature of SI algorithms and conclude that the PSO and GWO SI algorithms are better than K-means, and among those algorithms, the PSO performs best in terms of finding the optimal solution.

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Grey wolf optimizer (GWO) for automated offshore crane design

Cited by 23 publications

References 10 publications

A literature Survey on Well-known Algorithms of Optimum Power Flow

A literature Survey on Well-known Algorithms of Optimum Power Flow

Metaheuristic Parameter Identification of Motors Using Dynamic Response Relations

Swarm Intelligence Algorithms in Text Document Clustering with Various Benchmarks

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