Comparative Analysis of PSO Variants for Efficient Optimization of Hydrogen IC Engines

This study aims to optimize injection and ignition timings in hydrogen internal combustion engines through the Engine Control Module (ECM). Optimization in terms of computational time is essential for seamless engine performance. This work considers an optimization problem formulated in [International Journal of Hydrogen Energy, Volume 48, Issue 25, 2023, 9462–9473], which has been solved using the Multiple Populations Genetic Algorithm (MPGA). The present work provides a comparative study between MPGA and different variants of the Particle Swarm Optimization (PSO). Ultimately, we found that a suitable variant of Dynamic Multi-Swarm Particle Swarm Optimization (DMS-PSO) is comparable to MPGA in terms of convergence rate versus the number of generations and is superior in both computational time and convergence rate against mean time.