Combining particle swarm with ordinal optimization for stochastic simulation optimization problems

In this paper, we combine the particle swarm (PS) with ordinal optimization (OO), abbreviated as CPSOO, to solve for a good enough solution of the stochastic simulation optimization problem (SSOP) with huge search space. First, a rough model using stochastic simulation with a small amount of test samples will be used as a fitness function evaluation in particle swarm optimization (PSO) algorithm to select N roughly good solutions from search space. Next, starting from the selected N roughly good solutions we proceed with goal softening procedure to search for a good enough solution. Finally, the proposed CPSOO algorithm is applied to a centralized broadband wireless network with k-limited service discipline, which is formulated as a SSOP that consists of a huge discrete search space comprised by the vector of k-limited service discipline. The vector of good enough k-limited service discipline obtained by the proposed algorithm is promising in the aspects of solution quality and computational efficiency.