Understating continuous ant colony optimization for neural network training: A case study on intelligent sensing of manhole gas components

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 %its performance evaluation was well studied. Moreover, complexity analysis of ACO^* was firmly addressed. %in this article. 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.