Sharif Digital Repository

Particle Swarm Optimization (PSO) algorithms recently invented as intelligent optimizers with several highly desirable attributes. In this paper, two new hybrid Particle Swam Optimization schemes are proposed. The proposed hybrid algorithms are based on using the Particle Swarm Optimization techniques in conjunction with the Simulated Annealing (SA) approach. By simulating three different test functions, it is shown how the proposed hybrid algorithms offer the capability of converging toward the global minimum or maximum points. More importantly, the simulation results indicate that the proposed hybrid particle swarm-based simulated annealing approaches have much superior convergence... 

The quantification of carbon dioxide (CO2) dissolution in oil is crucial in predicting the potential and long-term behavior of CO2 in reservoir during secondary and tertiary oil recovery. Accurate predicting carbon dioxide molecular diffusion coefficient is a key parameter during carbon dioxide injection into oil reservoirs. In this study a new model based on adaptive neuro-fuzzy inference systems (ANFIS) is designed and developed for accurate prediction of carbon dioxide diffusivity in oils at elevated temperature and pressures. Particle Swarm Optimization (PSO) as population based stochastic search algorithms was applied to obtain the optimal ANFIS model parameters. Furthermore, a simple...