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中文摘要：
针对惯性权重线性递减粒子群算法不能适应复杂的非线性优化搜索过程的问题，提出了一种基于Sigmoid函数和聚集距离变化率改变惯性权重的方法。为了解决算法后期易陷入局部最优的缺点，在算法后期引人了具有记忆能力的禁忌搜索算法。改进后的算法不仅综合了粒子群优化算法的快速性、随机性和全局收敛性的优点，而且还具有禁忌搜索局部寻优的能力。测试函数仿真结果表明，改进后的算法不仅较好地避免了陷入局部最优，而且收敛速度也有提高。
英文摘要：
Due to the problem that the linearly decreasing weight of the Particle Swarm Optimization algorithm cannot adapt to the complex and nonlinear optimization process,a new method based on Sigmoid and the rate of cluster focus distance changing inertia weight was proposed. In order to solve its local search ability at the end of the run, the paper introduces tabu search at the end of run. The algorithm combines the particle swarm optimization algorithm of the fast, random and global convergence and the tabu search of local search ability. The experimental results show that the algorithm not only avoids falling into local optimization but also improves the optimal speed.
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