HECO-PSO: Hierarchical and Stagnation-Aware Particle Swarm Optimization With Elite-Guided Crossover

Abstract

This paper proposes a Hierarchical and Stagnation-Aware Particle Swarm Optimization with Elite-Guided Crossover (HECO-PSO). HECO-PSO dynamically partitions the swarm into multiple parallel layers, enabling independent exploration and periodic information exchange to mitigate premature convergence. Within each layer, elite-guided crossover integrates a particle's best position with that of the layer's elite particle, generating high-quality guiding vectors to enhance solution quality and maintain diversity. Building on this hierarchical framework, a stagnation-aware strategy adaptively intensifies guidance: prolonged stagnation activates crossover with the global elite, while severe stagnation directly assigns the global best guiding vector, thereby strengthening the ability to escape local optima. Experimental results on the CEC 2017 benchmark suite demonstrate HECO-PSO's competitive convergence speed and solution accuracy. Its effectiveness is further validated through a real-world UNSW-NB15 feature selection task, confirming adaptability and competitiveness in practical applications.