Abstract:

Inspired by the migratory behavior in the nature, a novel migrated particle swarm optimization (MPSO) algorithm is proposed. In this new algorithm, the population is randomly partitioned into several sub-swarms, each of which is made to evolve based on particle swarm optimization with time varying inertia weight and acceleration coefficients (LPSO-TVAC). At periodic stage in the evolution, some particles randomly migrate from one complex to another to enhance the diversity of the population and avoid premature convergence. It further improves the ability of exploration and exploitation. Simulations for benchmark test functions illustrate that the proposed algorithm (MPSO) possesses better ability to find the global optima than other variants and is an effective global optimization tool. Then the new algorithm is applied to parameters inversion of rheological model of rockfill. The results show that MPSO remarkably improves the calculation accuracy and is an effective tool in parameter inversion of complex model.

Key words: rheological model, parameter inversion, particle swarm optimization (PSO) algorithm, particle migration, adaptive mutation