AGWO: Advanced GWO in multi-layer perception optimization

Meng, Xianqiu; Jiang, Jianhua; Wang, Huan

doi:10.1016/j.eswa.2021.114676

Cited by 34 publications

(14 citation statements)

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“…The main optimization process can be divided into four stages. 26 (1) Encircling prey The position of each gray wolf in the search space is updated according to the position of the prey. The update equation is as follows: where t is the number of iterations, X p is the position of the prey, X is the position of the gray wolf, and D is the distance between the prey and the gray wolf, which is defined as follows:…”

Section: Modeling Techniquesmentioning

confidence: 99%

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Modeling the Solubility of Sulfur in Sour Gas Mixtures Using Improved Support Vector Machine Methods

et al. 2021

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The study of sulfur solubility is of great significance to the safe development of sulfur-containing gas reservoirs. However, due to measurement difficulties, experimental research data on sulfur solubility thus far are limited. Under the research background of small samples and poor information, a weighted least-squares support vector machine (WLSSVM)-based machine learning model suitable for a wide temperature and pressure range is proposed to improve the prediction accuracy of sulfur solubility in sour gas. First, we use the comprehensive gray relational analysis method to extract important factors affecting sulfur solubility as the model input parameters. Then, we use the whale optimization algorithm (WOA) and gray wolf optimizer (GWO) intelligence algorithms to find the optimal solution of the penalty factor and kernel coefficient and bring them into three common kernel functions. The optimal kernel function is calculated, and the final WOA-WLSSVM and GWO-WLSSVM models are established. Finally, four evaluation indicators and an outlier diagnostic method are introduced to test the proposed model’s performance. The empirical results show that the WOA-WLSSVM model has better performance and reliability; the average absolute relative deviation is as low as 3.45%, determination coefficient (R 2) is as high as 0.9987, and the prediction accuracy is much higher than that of other models.

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Section: Modeling Techniquesmentioning

confidence: 99%

“…The algorithm mechanism is shown in Figure . The main optimization process can be divided into four stages Encircling prey …”

Section: Modeling Techniquesmentioning

confidence: 99%

Modeling the Solubility of Sulfur in Sour Gas Mixtures Using Improved Support Vector Machine Methods

et al. 2021

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“…The MLP with a hidden layer is a typical architecture of the ANN. It needs to be optimized since it always falls into local optimum when dealing with massive data (Meng et al, 2021). In solving specific problems, traditional swarm intelligence methods are relatively perfect and mature (Ertenlice & Kalayci, 2018).…”

Section: Introductionmentioning

confidence: 99%

“…In solving specific problems, traditional swarm intelligence methods are relatively perfect and mature (Ertenlice & Kalayci, 2018). This triggered us to utilize the swarm intelligence methods to optimize the weights and biases term of MLP to achieve the optimal performance (Meng et al, 2021; Mirjalili et al, 2012). Because of the merits, MLP can effectively prevent credit risks and reduce the non-performing loan ratio.…”

Section: Introductionmentioning

confidence: 99%

An Enhanced TSA-MLP Model for Identifying Credit Default Problems

et al. 2022

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Credit default has always been one of the critical factors in the development of personal credit business. By establishing a default identification model, default can be avoided effectively. There are some existing methods to identify credit default. However, these methods have some problems: Problem (1): It is different to deal the non-linear data, Problem (2): The local stagnation results in the high error rate, and Problem (3): The premature convergence leads to the low classification rate. In this paper, the sinhTSA-MLP default risk identification model is proposed to solve these problems. In this model, the proposed sinhTSA method can effectively avoid the problems of falling into local optimum and premature convergence. And the benchmark test results demonstrate sinhTSA is superior to other methods. According to the two experiments, the classification rate reaches 77.35% and 96.48%. Therefore, the sinhTSA-MLP default identification model has some particular advantages in identifying credit problems The feasibility of the sinhTSA-MLP default identification model has been proved through helping to manage credit default more consciously.

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“…It has been used for solving various types of engineering problems in the literature. Some problems solved by GWO in the recent studies can be given as pressure vessel design, 60 clustering optimization, 64 inexact circuit design, 65 multilayer perception optimization, 66 and power scheduling problem. 67 GWO is modeled according to the hunting behaviors of the gray wolves that are hunter-animals located at the top of the food chain.…”

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confidence: 99%

A novel approach for nature‐based optimization algorithms: The threat factor approach

Toz

2021

Concurrency and Computation

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Nature-inspired optimization algorithms especially those based on the hunting behaviors of the creatures assume that the hunting operations are performed in a safe environment. However, generally, there are threats in real-life for the hunter-animals. This paper focuses on these threat factors and proposes that they can be used to improve the searching abilities of the algorithms. Gray wolf optimization (GWO) algorithm was selected to present the proposed approach and it was assumed that there was a mountain lion as the threat factor living in the same habitat with the wolf pack. The relations between the two predators were modeled and used to improve the performance of the algorithm. Five experiments were conducted to test the performance of the proposed method and the results were compared with the GWO and four optimization algorithms from the literature. It is shown that the proposed algorithm obtained best results for 21 of the 50 benchmark functions, while its closest competitor achieved the best results for 16 functions. Besides, the results of the Wilcoxon signed-rank test indicated that the proposed method is superior to all other methods. In addition, it was shown that the threat factor approach does not cause a significant increase in the processing time. K E Y W O R D SGWO algorithm, nature-inspired optimization, threat factor approach INTRODUCTIONNature is a great source for modeling optimization algorithms since there are so many creatures, events, or any other phenomena that can be used as the base for these algorithms. Therefore, nature-inspired optimization algorithms have been very popular over the past three decades.Generally, these algorithms can be divided into four classes as evolutionary-based, swarm-based, physics-based, and human-based algorithms. 1Evolutionary-based algorithms model natural evolution concepts such as mutation and natural selection. These algorithms are based on the rule of eliminating weak entities while providing that the strong individuals pass to the next generation. Genetic algorithm 2 which was proposed by Holland is the best-known evolutionary algorithm. differential evolution (DE), 3 genetic programming, 4 learner performance based behavior 5 algorithm, and biogeography-based optimizer 6 are some of the other algorithms based on the evolutionary approach. Swarm-based optimization algorithms are derived from the behaviors of the herds of the creatures to reach the food. Particle swarm optimization (PSO) is the most-known swarm-based optimization algorithm. PSO, simply, mimics the searching-food behaviors of a swarm of fish or birds. 7 Ant colony optimization, 8 artificial bee colony, 9gray wolf optimization (GWO), 10 ant lion optimization (ALO), 11 whale optimization algorithm (WOA), 12 dragonfly algorithm. 13 Moth-flame optimization (MFO), 14 salp swarm algorithm, 15 fish-swarm algorithm, 16 bird mating optimizer, 17 bat-inspired algorithm, 18 firefly algorithm, 19 naked mole-rat algorithm, 20 cuckoo search algorithm, 21 donkey and smuggler optimization, 22 and fit...

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AGWO: Advanced GWO in multi-layer perception optimization

Cited by 34 publications

References 80 publications

Modeling the Solubility of Sulfur in Sour Gas Mixtures Using Improved Support Vector Machine Methods

Modeling the Solubility of Sulfur in Sour Gas Mixtures Using Improved Support Vector Machine Methods

An Enhanced TSA-MLP Model for Identifying Credit Default Problems

A novel approach for nature‐based optimization algorithms: The threat factor approach

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