Enhanced grey wolf optimizer with a model for dynamically estimating the location of the prey

Luo, Kaiping

doi:10.1016/j.asoc.2019.01.025

Cited by 64 publications

(35 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…PSO, GWO, a continuous PSO with a local search (CPSO), an adapted Artificial Bee Colony for binary optimization (ABCbin), and Weighted Superposition Attraction (WSA). Luo [38] proposed an enhanced GWO (EGWO) model which dynamically estimates the location of the prey using a weight-based aggregation of the three dominant wolf leaders. The weights are generated using normalized random numbers within [0, 1].…”

Section: B Gwo Variantsmentioning

confidence: 99%

“…Our literature review indicates that, instead of tackling both abovementioned problems collectively, most of the existing studies focus only on one problem, i.e. either enhancing search diversity and exploration capability via the employment of multiple position updating strategies [42,43,45], or improving local exploitation by reinforcing the domination of the leader (wolf α) in the leadership hierarchy [37,38].…”

Section: B Gwo Variantsmentioning

confidence: 99%

See 1 more Smart Citation

Evolving CNN-LSTM Models for Time Series Prediction Using Enhanced Grey Wolf Optimizer

2020

View full text Add to dashboard Cite

In this research, we propose an enhanced Grey Wolf Optimizer (GWO) for designing the evolving Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) networks for time series analysis. To overcome the probability of stagnation at local optima and a slow convergence rate of the classical GWO algorithm, the newly proposed variant incorporates four distinctive search mechanisms. They comprise a nonlinear exploration scheme for dynamic search territory adjustment, a chaotic leadership dispatching strategy among the dominant wolves, a rectified spiral local exploitation action, as well as probability distribution-based leader enhancement. The evolving CNN-LSTM models are subsequently devised using the proposed GWO variant, where the network topology and learning hyperparameters are optimized for time series prediction and classification tasks. Evaluated using a number of benchmark problems, the proposed GWO-optimized CNN-LSTM models produce statistically significant results over those from several classical search methods and advanced GWO and Particle Swarm Optimization variants. Comparing with the baseline methods, the CNN-LSTM networks devised by the proposed GWO variant offer better representational capacities to not only capture the vital feature interactions, but also encapsulate the sophisticated dependencies in complex temporal contexts for undertaking time-series tasks.

show abstract

Section: B Gwo Variantsmentioning

confidence: 99%

Section: B Gwo Variantsmentioning

confidence: 99%

Evolving CNN-LSTM Models for Time Series Prediction Using Enhanced Grey Wolf Optimizer

2020

View full text Add to dashboard Cite

show abstract

“…In GWO algorithm, search process can be performed by wolf sets as solutions that deployed in search area randomly [29]. The main steps of grey wolves hunting are including searching prey, tracking, encircling, and then attacking [31]. The mathematical model of GWO and novel method stated at subsection 4.2.2.…”

Section: Classical Gwo Algorithmmentioning

confidence: 99%

Computational intelligence-based connectivity restoration in wireless sensor and actor networks

Mohammadi

Farahani

2020

J Wireless Com Network

View full text Add to dashboard Cite

Network failure is categorized into the two types of software and hardware (physical layer) failure. This paper focuses on the physical layer failure in the wireless sensor and actor networks (WSANs). Actors play an important role in data processing, decision-making, and performing appropriate reactions. Single or multiple nodes failure of actors due to the explosion, energy depletion, or harsh environments, can cause multiple disjoint partitions. This paper has proposed a new computational intelligence-based connectivity restoration (CICR) method. It uses a combination of advanced computational intelligence methods to solve restoration problem. The proposed algorithm applies the novel enhanced Lagrangian relaxation with a novel metaheuristic sequential improved grey wolf optimizer (SIGWO) search space algorithm in simultaneous selection of k sponsor and p pathway nodes. The reactive proposed method aims to reduce the travel distance or moving cost and communication cost. As a result, the restored network has minimum of topology change and energy consumption. In terms of total traveled distance, CICR has 37.19%, 71.47%, and 44.71% improvement in the single-node failure averagely in comparison with HCR, HCARE, and CMH, respectively. Also, it has an average of 61.54%, 40.1%, and 57.76% improvement in comparison with DCR, PRACAR, and RTN in multiple partitions resulted from multiple nodes failure, respectively. The reliability of CICR method has improved averagely by 35.85%, 38.46%, 22.03% over HCR, CMH, and HCARE in single-node failure. In multiple nodes failure, reliability of CICR has averagely 61.54% and 20% over DCR and PRACAR, respectively.

show abstract

“…kNN: Many researchers have tried to use kNN classifier for pattern recognition and classification with respect to the training data [21]. The result of comparing the fuzzy version with the Crisp version shows that the fuzzy algorithm has a lower error rate.…”

Section: Classifiersmentioning

confidence: 99%

Classification and Prediction of Student Academic Performance using Gray Wolf Optimization Based Relief-F Budget Random Forest

Deepika*¹,

Sathyanarayana²

2019

IJRTE

View full text Add to dashboard Cite

The student academic prediction model helps to predict the student performance that helps the university to provide necessary care to the particular students. Efficient prediction model helps to encourage the student for better performance in the academic. In this research, the Relief-F Budget Tree Random Forest with Gray Wolf Optimization (RFBTRF-GWO) method is proposed for the feature selection. The Gray Wolf Optimization (GWO) helps to scale the relevant feature with ranking order from the features selected by the Relief-F Budget Tree Random Forest (RFBTRF). The selected features are given as input to the classifier for the effective prediction. The k-Nearest Neighbor (kNN) and Artificial Neural Network (ANN) are used for the classification. The proposed RFBTRF-GWO method is evaluated on the three datasets such as two UCI datasets and one collected dataset. The RFBTRF-GWO has a higher performance accuracy of 96.2 % while the existing method RFBTRF has an accuracy of 70.88 %.

show abstract

Enhanced grey wolf optimizer with a model for dynamically estimating the location of the prey

Cited by 64 publications

References 41 publications

Evolving CNN-LSTM Models for Time Series Prediction Using Enhanced Grey Wolf Optimizer

Evolving CNN-LSTM Models for Time Series Prediction Using Enhanced Grey Wolf Optimizer

Computational intelligence-based connectivity restoration in wireless sensor and actor networks

Classification and Prediction of Student Academic Performance using Gray Wolf Optimization Based Relief-F Budget Random Forest

Contact Info

Product

Resources

About