Asynchronous particle swarm optimization with discrete crossover

Recent work has evaluated the performance of a synchronous global best (gbest) particle swarm optimization (PSO) algorithm hybridized with discrete crossover operators. This paper investigates if using asynchronous position updates instead of synchronous updates will result in improved performance of a gbest PSO that uses these discrete crossover operators. Empirical analysis of the performance of the resulting algorithms provides strong evidence that asynchronous position updates significantly improves performance of the PSO discrete crossover hybrid algorithms, mainly with respect to accuracy and convergence speed. These improvements were seen over an extensive benchmark suite of 60 boundary constrained minimization problems of various characteristics.