A New Hybrid Whale Optimizer Algorithm with Mean Strategy of Grey Wolf Optimizer for Global Optimization

Singh, Narinder; Hachimi, Hanaâ

doi:10.3390/mca23010014

Cited by 37 publications

(25 citation statements)

References 57 publications

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“…Delta wolves control omega, but they must undergo alpha and beta orders. Delta shall be responsible for guards, scouts, hunters, elders, and patients and each has its own speci ic responsibility [7].…”

Section: Grey Wolf Optimization (Gwo)mentioning

confidence: 99%

Improving cancer diseases classification using a hybrid filter and wrapper feature subset selection

Muhammed¹,

Saber²

2020

Ann Proteom Bioinform

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In the classifi cation of cancer data sets, we note that they contain a number of additional features that infl uence the classifi cation accuracy. There are many evolutionary algorithms that are used to defi ne the feature and reduce dimensional patterns such as the gray wolf algorithm (GWO) after converting it from a continuous space to a discrete space. In this paper, a method of feature selection was proposed through two consecutive stages in the fi rst stage, the fuzzy mutual information (FMI) technique is used to determine the most important feature selection of diseases dataset through a fuzzy model that was built based on the data size. In the second stage, the binary gray wolf optimization (BGWO) algorithm is used to determine a specifi c number of features affecting the process of classifi cation, which came from the fi rst stage. The proposed algorithm, FMI_BGWO, describes effi ciency and effectiveness by obtaining a higher classifi cation accuracy and a small number of selected genes compared to other competitor algorithms.

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Section: Grey Wolf Optimization (Gwo)mentioning

confidence: 99%

Improving cancer diseases classification using a hybrid filter and wrapper feature subset selection

Muhammed¹,

Saber²

2020

Ann Proteom Bioinform

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“…The datasets are a synthesis dataset, 3-bits XOR, a small dataset, Iris, and three bio-medical datasets; Balloon, Breast cancer and Heart. The details of these datasets and parameter setting including the MLP structure for solving these dataset are taken from the literature [53,54]. The brief details of dataset for the implementation and comparison performance of algorithms are presented in Table 14.…”

Section: Bio-medical Real Life Classification Problemsmentioning

confidence: 99%

“…The brief details of dataset for the implementation and comparison performance of algorithms are presented in Table 14. CPFOA is compared with six meta-heuristic algorithms; JADE, BLPSO, CLPSO, GWO, MGWO and HAGWO [54]. HAGWO was successfully test with these five datasets.…”

Section: Bio-medical Real Life Classification Problemsmentioning

confidence: 99%

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A Cooperation of Multileader Fruit Fly and the Probabilistic Random Walk with Adaptive Normalization for Solving Solution of the Unconstrained Optimization Problems

Apinantanakon¹,

Sunat²,

Chiewchanwattana³

2018

Preprint

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A swarm based nature-inspired optimization algorithm namely fruit fly optimization algorithm (FOA) has simple structure and ease of implementation. However, FOA has a low success rate and a slow convergence because FOA generates new positions around the best location using fixed search radius. Several improved FOAs have been proposed. But their exploration ability is questionable. To make the search process to transit from the exploration phase to the exploitation phase smoothly, this paper proposes a new FOA constructed from a cooperation of the multileader and the probabilistic random walk strategies (CPFOA). It has two population types working together. CPFOA's performance is evaluated by 18 well-known standard benchmark, and 30 CEC’2017 functions. The results showed that CPFOA outperforms both the original FOA and its variants in terms of convergence speed and performance accuracy. The results base on CEC’2017 show that CPFOA can achieve a very promising accuracy when compared with the well-known competitive algorithms. CPFOA is applied to optimize two applications; the MLPs classifying real datasets and extracting parameters of T-S fuzzy system for modelling Box and Jenkins gas furnace data set. CPFOA can find parameters having a very high quality compared with the best known competitive algorithms.

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“…Particle Swarm Optimization (HPSO) [21], Hybrid MGBPSO-GSA [22], MGWO [23],HPSOGWO [24], HGWOSCA [25] and HAGWO [26] and many others.…”

Section: Introductionmentioning

confidence: 99%

A Modified Variant of Grey Wolf Optimizer

Singh

2018

Scientia Iranica

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A New Hybrid Whale Optimizer Algorithm with Mean Strategy of Grey Wolf Optimizer for Global Optimization

Cited by 37 publications

References 57 publications

Improving cancer diseases classification using a hybrid filter and wrapper feature subset selection

Improving cancer diseases classification using a hybrid filter and wrapper feature subset selection

A Cooperation of Multileader Fruit Fly and the Probabilistic Random Walk with Adaptive Normalization for Solving Solution of the Unconstrained Optimization Problems

A Modified Variant of Grey Wolf Optimizer

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