A Modified Variant of Grey Wolf Optimizer

Singh, Narinder

doi:10.24200/sci.2018.50122.1523

Cited by 17 publications

(13 citation statements)

References 44 publications

(42 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Babu et al [14] provides 98.23% using GWO optimization using RNN. The original version of the grey wolf optimization (GWO) algorithm has few disadvantages such as low solving accuracy, bad local searching ability and slow convergence rate [15], [16]. Krishnan et al [5] has developed an improvised RNN with multiple GRU with 98.4%.…”

Section: Critical Analysismentioning

confidence: 99%

“…Removing the attributes from the datasets makes the quality of the prediction poor. The original version of the GWO algorithm has a few disadvantages such as low solving accuracy, unsatisfactory ability of local searching, and slow convergence rate [15], [16]. The Advance RNN model with multiple GRU has produced 98.4% yet the model consumes high time processing.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Hybrid deep learning model using recurrent neural network and gated recurrent unit for heart disease prediction

Krishnan

Magalingam

Ibrahim

2021

IJECE

View full text Add to dashboard Cite

<span>This paper proposes a new hybrid deep learning model for heart disease prediction using recurrent neural network (RNN) with the combination of multiple gated recurrent units (GRU), long short-term memory (LSTM) and Adam optimizer. This proposed model resulted in an outstanding accuracy of 98.6876% which is the highest in the existing model of RNN. The model was developed in Python 3.7 by integrating RNN in multiple GRU that operates in Keras and Tensorflow as the backend for deep learning process, supported by various Python libraries. The recent existing models using RNN have reached an accuracy of 98.23% and deep neural network (DNN) has reached 98.5%. The common drawbacks of the existing models are low accuracy due to the complex build-up of the neural network, high number of neurons with redundancy in the neural network model and imbalance datasets of Cleveland. Experiments were conducted with various customized model, where results showed that the proposed model using RNN and multiple GRU with synthetic minority oversampling technique (SMOTe) has reached the best performance level. This is the highest accuracy result for RNN using Cleveland datasets and much promising for making an early heart disease prediction for the patients.</span>

show abstract

Section: Critical Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hybrid deep learning model using recurrent neural network and gated recurrent unit for heart disease prediction

Krishnan

Magalingam

Ibrahim

2021

IJECE

View full text Add to dashboard Cite

show abstract

“…Grey‐wolf algorithm has low solving accuracy and bad local searching ability. So we decided to go with more refined optimization algorithms 26 . In this article, we have implemented the Genetic Algorithm (GA), APSO, Simulated Annealing (SA), and ACO to solve the following function for the shortest path.…”

Section: Proposed Workmentioning

confidence: 99%

Implementation and comparative analysis of evolutionary algorithms for energy optimization in wireless sensor networks

Kundaliya

Hadia

2021

Int J Communication

View full text Add to dashboard Cite

Summary Wireless sensor networks will be at the epitome of applications in near future. It is going through tremendous positive changes. Although it suffers from some limitations and it sorts out its limitations day by day. The biggest limitation of wireless sensor networks is the limited energy of the nodes. Most of the energy is used during the routing of the data. An optimized way of routing will save the valuable energy of the node and helpful for increasing the network's lifetime. The optimization algorithms are indeed utterly helpful in many applications across the different fields to optimize the resources. In the proposed worked we have applied Adaptive Particle Swarm Optimization (APSO), Ant Colony Optimization (ACO), Genetic Algorithms (GA), and Simulated Annealing (SA) to a Modified Rendezvous Point Selection Scheme and observed the effect on the network lifetime and energy consumption in the network. We have made an exhaustive comparison of all the optimization algorithms with considerable simulations.

show abstract

“…Singh proposed a modified variant of GWO to solve the optimization problem. The better performance of convergence and better accuracy in results provided the best optimal solution 92 . Sathish et al proposed hybrid modal of GWO to recover the accuracy parameter for detection in designing the efficient intrusion detection model 93…”

Section: Literature Reviewmentioning

confidence: 99%

“…The better performance of convergence and better accuracy in results provided the best optimal solution. 92 Sathish et al proposed hybrid modal of GWO to recover the accuracy parameter for detection in designing the efficient intrusion detection model. 93 Further, the classical HHO and its different variants have been applied to various engineering optimization problems, namely, Abbasi et al proposed the applications of the algorithm Harris hawks optimizer to design the microchannel heat sinks to decrease the generation to entropy.…”

Section: Literature Reviewmentioning

confidence: 99%

ACanis lupusinspired upgraded Harris hawks optimizer for nonlinear, constrained, continuous, and discrete engineering design problem

Nandi

Kamboj

2020

Numerical Meth Engineering

View full text Add to dashboard Cite

Recently established Harris hawks optimization (HHO) has natural behavior for finding an optimum solution in global search space without getting trapped in previous convergence. However, the exploitation phase of the current Harris hawks optimizer algorithm is poor. In the present research, an improved version of the HHO algorithm, which combines Harris hawks optimizer with Canis lupus inspire grey wolf optimizer (GWO) and named as hHHO-GWO algorithm, has been proposed to find the solution of various optimization problems such as nonlinear, nonconvex, and highly constrained engineering design problem. In the proposed research, the phase of exploration and exploitation of the existing HHO algorithm has been further improved using GWO algorithm and its performance has been tested for various benchmarks problems including CEC2005 (unimodal, multimodal, and fixed dimensions functions), multimodal functions with variable dimensions, and CEC-BC-2017 test functions. Further, the developed hybrid optimizer has been tested for 11 different engineering design and optimization problems and experimental results of hHHO-GWO have been compared with other optimizer.

show abstract

A Modified Variant of Grey Wolf Optimizer

Cited by 17 publications

References 44 publications

Hybrid deep learning model using recurrent neural network and gated recurrent unit for heart disease prediction

Hybrid deep learning model using recurrent neural network and gated recurrent unit for heart disease prediction

Implementation and comparative analysis of evolutionary algorithms for energy optimization in wireless sensor networks

ACanis lupusinspired upgraded Harris hawks optimizer for nonlinear, constrained, continuous, and discrete engineering design problem

Contact Info

Product

Resources

About