Affiliations: [a] IT4Innovations, VŠB Technical University of Ostrava, Ostrava, Czech Republic | [b] Department of Computer Science and Engineering, Jadavpur University, Kolkata, India | [c] Department of Computer and System Sciences, Visva-Bharati University, India | [d] CEGESS, Indian Institute of Engineering Science and Technology, Howrah, India
Correspondence:
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Corresponding author: Varun Kumar Ojha, IT4Innovations, VŠB Technical University of Ostrava, Ostrava, Czech Republic. E-mail:[email protected]
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 %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.
Keywords: Continuous ant colony optimization, neural network, intelligent system, gas detection, complexity analysis
