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How effective is the Grey Wolf optimizer in training multi-layer perceptrons
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How effective is the Grey Wolf optimizer in training multi-layer perceptrons
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Journal Article
How effective is the Grey Wolf optimizer in training multi-layer perceptrons
2015
Overview
This paper employs the recently proposed Grey Wolf Optimizer (GWO) for training Multi-Layer Perceptron (MLP) for the first time. Eight standard datasets including five classification and three function-approximation datasets are utilized to benchmark the performance of the proposed method. For verification, the results are compared with some of the most well-known evolutionary trainers: Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Ant Colony Optimization (ACO), Evolution Strategy (ES), and Population-based Incremental Learning (PBIL). The statistical results prove the GWO algorithm is able to provide very competitive results in terms of improved local optima avoidance. The results also demonstrate a high level of accuracy in classification and approximation of the proposed trainer.
Publisher
Springer US,Springer Nature B.V
