Enhanced water cycle algorithm using Hookes and Jeeves method for clustering large gas data

• Main Authors: Taib, Hasnanizan, Bahreininejad, Ardeshir
• Format: Conference Proceeding
• Language: English
• Subjects: Algorithms; Cluster analysis; Clustering; Convergence; Data analysis; Data mining; Datasets; Euclidean geometry; Evaporation rate; Gas turbines; Heuristic methods; Hydrologic cycle; Machine learning; Optimization; Performance evaluation; Search methods
• ISSN: 0094-243X; 1551-7616
• DOI: 10.1063/5.0110484

Numerous researchers have applied nature-inspired population-based metaheuristics for solving optimization problems including data clustering. However, the issues of premature convergence and slow convergence rate can still occur when these promising search methods are applied to complex and large data-clustering problems, including the evaporation-rate based water cycle algorithm, WCAER. In this paper, a recently proposed hybrid version of WCAER in conjunction with a local search method named Hookes and Jeeves method was further tested to perform data clustering for large dataset. The proposed hybrid algorithm is experimented on the gas turbine emission data that contains 36733 instances of 11 sensor measures aggregated over one hour, from a gas turbine in Turkey, available from the UCI machine-learning repository. The simulation results confirm the superiority of the hybrid method as an efficient and reliable algorithm to solve gas turbine clustering problem, in comparison to the original evaporation-rate based water cycle algorithm, in terms of solution quality as well as computational performance formulated from two applied objective functions namely the Euclidean distance and the Davies-Bouldin index. Thus, the outcome of the study generally provides some performance evaluation of WCAER for large dataset when applied in cluster analysis, a valuable data analysis and data mining technique. It is hoped that other metaheuristics could be applied to the 5 years span large dataset for performance comparison with other further considerations such as using various suitable objective functions and cluster validity indices.