Multi objective Metaheuristic Approaches for Data Clustering in Engineering Applications

Abstract

Clustering is the process of combining similar data objects into a number of groups (called clusters). Clusters are formed in such a way that data objects having similar nature are kept in one cluster and are dissimilar to data objects of other clusters. This thesis focuses on the concept of clustering, i.e., automatically determining the number of features and number of clusters. Due to unknown number of cluster, clustering technique is treated as NP-hard problem. To solve this problem, metaheuristic algorithms are utilized. In this thesis, two novel bio-inspired metaheuristic optimization algorithms have been proposed namely Spotted Hyena Optimizer (SHO) and Emperor Penguin Optimizer (EPO) for solving real-life engineering design problems. The proposed approaches are assessed on standard benchmark test functions. The convergence and computational complexity have also been analyzed to ensure the applicability of proposed algorithms. The performance of the proposed algorithms are analysed and compared with different algorithms such as GWO, PSO, MFO, MVO, SCA, GSA, GA, and HS. Experimental results reveal that the proposed algorithms are able to solve constrained and unconstrained engineering design problems. In addition to above-proposed algorithms, a multi-objective version of spotted hyena optimizer is proposed and named as Multi-objective Spotted Hyena Optimizer (MOSHO). In order to determine the better solution, the concept of Pareto dominance is utilized in the proposed approach. An external repository is used to store the Pareto optimal solutions. An adaptive grid mechanism is used to produce the distributed Pareto fronts and improve diversity. Moreover, the group selection mechanism is also employed for better convergence. The roulette wheel mechanism is used to select the effective solutions from archive to simulate the social and hunting behaviors of spotted hyenas. The proposed algorithm has been tested on multi-objective benchmark test functions and then applied on constrained engineering design problem