Paper Info
Title
Understating continuous ant colony optimization for neural network training: A case study on intelligent sensing of manhole gas components.
Abstract
In this work, we proposed various strategies for improving the performance of continuous ant colony optimization algorithm (ACO∗), which was used here for optimizing neural network (NN). Here, a real-world problem, that is, detection of manhole gas components, was used for case study. Manhole contains various toxic and explosive gases. Therefore, pre-detection of these toxic gases is crucial to avoid human fatality. Hence, we proposed to design an intelligent sensory system, which used a trained NN for detecting manhole gases. The training to NN was provided using dataset that was generated using laboratory tests, sensor’s data-sheets, and literature. The primary focus of this work was on the performance evaluation and improvement of ACO∗ algorithm. Hence, understanding of ACO∗ parameter tuning and enhancements of ACO∗ parameters through performance evaluation was well studied. Moreover, complexity analysis of ACO∗ was firmly addressed. We extended our article scope to cover the performance comparisons between ACO∗ and other NN training algorithms. We found that the improved ACO∗ performed best in comparison to other NN training algorithms such as backpropagation, conjugate gradient, particle swarm optimization, simulated annealing, and genetic algorithm.
Year
Venue
Field
2016
Int. J. Hybrid Intell. Syst.
Simulated annealing,Ant colony optimization algorithms,Particle swarm optimization,Conjugate gradient method,Computer science,Explosive material,Artificial intelligence,Artificial neural network,Backpropagation,Machine learning,Genetic algorithm
DocType
Volume
Issue
Journal
12
4
Citations 
PageRank 
References 
3
0.40
9
Authors
4
Name
Order
Citations
PageRank
Varun Kumar Ojha1329.25
Paramartha Dutta210020.77
Atal Chaudhuri3104.67
H. Saha4468.61