A Monte Carlo simulation based chaotic differential evolution algorithm for scheduling a stochastic parallel processor system
A simulation-based evolutionary optimization is proposed for a parallel processor system. A lower level Monte Carlo and an upper level differential evolution are suggested. Simulation is used to assess quality of candidate solutions and optimizer is utilized to guide the search at upper level. Chaos theory is employed to enhance quality of results via preventing premature convergence and locality.
differential evolution chaos theory chaotic maps simulation-based optimization parallel processor system scheduling