Management rules optimization for software development based on System Dynamics and Swarm intelligence

Software research and development has to take considerable costs and time. For traditional decision making, the experience of managers is a crucial key to make accurate decisions. In contrast, modern entrepreneurs employ database and statistical model to determine efficiently favorable strategies. The main goal of this study is to build system equation models by limited but informational data and to transform observed mathematical results into pragmatic plans for decision makers. Here we apply the simulation of system dynamics to build Abdel-Hamidpsilas dynamics model and to create pseudo-outcomes requisite for iteration method of particle swarm optimization (PSO), and then to search the optimization solution by PSO algorithm. The results seem to indicate that PSO algorithm is competitive with other evolutionary algorithms, and can be successfully applied to more data mining domains.