Parameter Adaptation of Enhanced Ant Colony System for Water Quality Rules Classification

Water quality monitoring in aquaculture involves classifying and analyzing the collected data to assess the water quality that is appropriate for breeding, rearing and harvesting aquatic organisms. Systematic data classification is essential when it comes to managing large amounts of data that are continuously sensed in real time and have various attributes in each instance of a sequence. Ant Colony System (ACS) has been employed in optimizing the data classification in smart aquaculture, where the majority of the research focuses on enhancing the classification procedure using predetermined parameters within a specified range. Nevertheless, this approach does not guarantee ideal performance. This paper enhances the ACS algorithm by introducing the Enhanced Ant Colony System-Rule Classification (EACS-RC) algorithm, which improves rule construction by integrating pheromone and heuristic values while incorporating advanced pheromone update techniques. The optimal parameter values to be used by the proposed algorithm are obtained from parameter adaptation experiments in which different values within the defined range were applied to obtain the optimal value for each parameter. Experiments were performed on the Kiribati water quality dataset and the results of the EACS-RC algorithm were evaluated against the AntMiner and AGI-AntMiner algorithms. Based on the results, the proposed algorithm outperforms the benchmark algorithms in classification accuracy and processing time. The output of this study can be adopted by the other ACS variants to achieve optimal performance for data classification in smart aquaculture.