中文摘要：

受自然界物种迁徙的启发，提出了一种新的改进的粒群优化算法（MPSO）。算法初始化时，将粒子随机地划分为若干个子粒群，每个子粒群按照给定的策略独立演化，在演化中的指定时段进行粒子的随机迁徙和自适应变异，以保持整个种群的多样性，避免早熟收敛。基准测试函数的计算结果表明，MPSO算法的性能优于其他几种改进算法。堆石体幂函数流变模型，参数较多，具有很强的非线性，将MPSO算法应用到堆石体幂函数流变模型的参数反演中。计算结果表明，利用反演的流变模型参数计算的坝体流变变形与实测变形在发展规律和数值上均比较吻合，证明MPSO算法在多参数、强非线性的复杂模型参数反演中的优越性。

英文摘要：

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 ofrockfill. The results show that MPSO remarkably improves the calculation accuracy and is an effective toot in parameter inversion of complex model.