A New Hybrid Optimization Algorithm to Solve the Clustering Problem

Abstract

This paper presents a novel hybrid technique, merging the K-means algorithm with Genetic Algorithm (GA), aiming to enhance clustering performance. This approach leverages the strengths of both algorithms, enabling improved cluster generation by overcoming individual algorithmic limitations. The GA-KM algorithm is introduced to aid K-means in avoiding local optima, with GA exhibiting proficiency in determining optimal cluster initialization and parameter optimization. The focus is on developing a GA-based algorithm for generating high-quality clusters efficiently. Notably, the research explores the application of this hybrid approach to address issues in the educational domain, specifically for out-of-school children. The fitness function in GA is tailored to the problem area, emphasizing the need for an appropriate system to study and address school children's problems. The research proposes a hybrid algorithm (KM-GA-NM-PSO) that amalgamates the best features of existing algorithms, thereby overcoming individual limitations and promising superior results. This hybridization is expected to yield high-quality clusters with minimal function evaluations, outperforming other methods by producing clusters with small standard deviations on selected datasets. The proposed approach, combining KM, GA, NM, and PSO algorithms, demonstrates improved data clustering quality and algorithmic efficiency.