MOCF: A Multi-Objective Clustering Framework using an Improved Particle Swarm Optimization Algorithm

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Gangolu Yedukondalu
K. Samunnisa
M. Bhavsingh
I S Raghuram
Addepalli Lavanya


Traditional clustering algorithms, such as K-Means, perform clustering with a single goal in mind. However, in many real-world applications, multiple objective functions must be considered at the same time. Furthermore, traditional clustering algorithms have drawbacks such as centroid selection, local optimal, and convergence. Particle Swarm Optimization (PSO)-based clustering approaches were developed to address these shortcomings. Animals and their social Behaviour, particularly bird flocking and fish schooling, inspire PSO. This paper proposes the Multi-Objective Clustering Framework (MOCF), an improved PSO-based framework. As an algorithm, a Particle Swarm Optimization (PSO) based Multi-Objective Clustering (PSO-MOC) is proposed. It significantly improves clustering efficiency. The proposed framework's performance is evaluated using a variety of real-world datasets. To test the performance of the proposed algorithm, a prototype application was built using the Python data science platform. The empirical results showed that multi-objective clustering outperformed its single-objective counterparts.

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How to Cite
Yedukondalu, G. ., K. Samunnisa, M. Bhavsingh, I. S. . Raghuram, and A. . Lavanya. “MOCF: A Multi-Objective Clustering Framework Using an Improved Particle Swarm Optimization Algorithm”. International Journal on Recent and Innovation Trends in Computing and Communication, vol. 10, no. 10, Oct. 2022, pp. 143-54, doi:10.17762/ijritcc.v10i10.5743.


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