中文摘要：

针对惯性权重线性递减粒子群算法（LDW）不能适应复杂的非线性优化搜索过程的问题，提出了一种非线性递减的惯性权重策略，使算法很快地进入局部搜索，并在算法中引入混合变异算子，克服算法易早熟收敛的缺陷。对几种典型函数的测试结果表明，本文算法的收敛速度和收敛精度都明显优于LDW算法。

英文摘要：

A new inertia weight with nonlinearly descending strategy was presented to solve the problem that the Linearly Decreasing Weight （LDW） of particle swarm algorithm could not adapt to the complex and nonlinear optimization process, which could quickly put the algortthm into local searching. Further more, mutation operator was introduced to overcome the problem of the premature and low precision of the standard PSO. The algorithm of LDW-PSO and our method were tested with five well-known benchmark functions. The experiments show that the convergence speed and accuracy of our method are significantly superior to that of LDW-PSO.