Particle Swarm Optimization Programming
PSO is a parallel stochastic optimization algorithm with advantages of less parameters and high efficiency. This paper describes the programming problem in the method of two linear tables with discrete and continuous quantity, then uses discrete PSO algorithm to discrete optimization and continuous PSO to optimize continuous quantity in the solving process respectively, based on these proposes the Particle Swarm Optimization Programming algorithm. Finally, GP and PSOP algorithms are compared by applying them to solving programming problem respectively with three typical test functions, the results show that the PSOP algorithm has better convergence precision and stability than the GP algorithm.