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This work investigates the potential of the particle swarm algorithm for the optimization of detailed kinetic mechanisms. To this end, empirical analysis has been conducted to evaluate the efficiency of this algorithm in comparison with the genetic algorithm (GA). Both algorithms are built on evolutionary processes according to which a randomly defined population will evolve, over the iterations, toward an optimal solution. The GA is driven by crossover and mutation operators and by a selection method. The particle swarm optimization (PSO) approach is based on the experience of each individual and on the group experience to control the direction of its evolution. The success of the application of an algorithm can be sensitive to the choice of operators and the relative importance attributed to them. Therefore, to make the comparison as rigorous as possible, about a dozen strategies were proposed for each algorithm and the performances were evaluated. A degraded version of the GRI-Mech 3.0 mechanism (i.e., containing some of the kinetic constants randomly modified) was generated and then optimized by the two evolutionary algorithms to recover the predictive character of the original mechanism. The results show that for the majority of the proposed strategies, PSO is more efficient than the GA, whereas the latter is generally much more used for the optimization of detailed kinetic mechanisms.

On the Potential of the Particle Swarm Algorithm for the Optimization of Detailed Kinetic Mechanisms. Comparison with the Genetic Algorithm