关键词: 粒子群优化算法, 惯性权重, 对数递减, 对数调整因子

Abstract: 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.

Key words: particle swarm optimization algorithm, inertia weight, logarithmic decreasing, logarithmic adjustment factor