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Swarm intelligence algorithms are widely recognized for their efficiency in solving complex optimization problems. However, their scalability poses challenges, particularly with large problem instances. This study investigates the time performance of swarm intelligence algorithms by leveraging parallel computing on both central processing units (CPUs) and graphics processing units (GPUs). The focus is on optimizing algorithms designed for range search in Euclidean space to enhance GPU execution. Additionally, the study explores swarm-inspired solutions specifically tailored for GPU implementations, emphasising improving efficiency in video rendering and computer simulations. The findings highlight the potential of GPU-accelerated swarm intelligence solutions to address scalability challenges in large-scale optimization, offering promising advancements in the field.