Adaptive Swarm Intelligence Algorithms for High-Dimensional Data Clustering in Big Data Analytics

The exponential growth of big data and increasing dimensionality pose significant challenges for traditional clustering algorithms, particularly in terms of computational efficiency and solution quality. This study addresses the critical limitations of existing swarm intelligence approaches by introducing an innovative Hybrid Adaptive Swarm Intelligence (HASI) algorithm for high-dimensional data clustering. The proposed method combines Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) with a novel adaptive dimensionality reduction mechanism, overcoming prevalent issues of premature convergence and scalability in complex data environments. By integrating a dynamic feature selection technique and implementing a distributed computing framework compatible with Apache Spark, the HASI algorithm demonstrates superior performance across multiple high-dimensional datasets. Experimental validation on synthetic and real-world big data benchmarks reveals that the proposed approach achieves up to 37% improvement in clustering accuracy and 52% reduction in computational complexity compared to state-of-the-art swarm intelligence clustering methods. The adaptive mechanism dynamically balances exploration and exploitation, enabling more robust and efficient clustering in high-dimensional spaces. The research contributes a scalable, adaptive swarm intelligence framework that significantly enhances clustering performance for big data analytics, offering a promising solution to the computational challenges inherent in high-dimensional data processing.