基于惯性权重对数递减的粒子群优化算法

针对粒子群算法收敛速度慢和易陷入局部最优的问题，提出了基于惯性权重对数递减的粒子群算法，并引入对数调整因子，对数调整因子的不同取值保证了算法搜索成功率。选取八种典型函数分别进行给定迭代次数和给定精度的仿真实验，并与标准PSO算法、惯性权重线性递减PSO算法、惯性权重高斯函数递减PSO算法进行比较。测试结果表明，该策略可以简便高效地提高算法的全局收敛性和收敛速度，并且具有较好的稳定性。求解大多数优化问题时，即使不引入对数调整因子新算法就可以获得较好的效果。

Particle swarm optimization algorithm based on inertia weight logarithmic decreasing

In the light of the problems of slow convergence rate and falling into the local optimization easily for the Particle Swarm Optimization （PSO） algorithm, the algorithm which based on the inertia weight logarithmic decreasing is proposed and the logarithmic adjustment factor is introduced. Changes of logarithmic adjustment factor ensure the success rate. The simulation experiment for eight kinds of typical function in the given number of iterations and precision and the comparison with standard, inertia weight linearly decreasing PSO algorithm and inertia weight decreasing based on Gaussian function are made. The test results show that, the strategy can improve the algorithm’s global convergence and convergence speed easily and efficiently and has a better stability. The better performances can be obtained even without introducing the logarithmic adjustment factor while solving the most optimization problems.