摘要： 为提高粒子群优化算法的全局搜索和局部开采能力，提出一种结合禁忌搜索(TS)的改进粒子群优化算法。在搜索过程中，以线性递增的概率对最优粒子实施随机扰动，在全局搜索收敛到一定程度后，引入TS算法进行局部搜索，使算法快速收敛到全局最优解。分析结果表明，该算法收敛精度较高，能有效克服早熟收敛问题。

关键词: 粒子群优化算法, 禁忌搜索, 随机扰动, 局部最优, 收敛精度

Abstract: To improve the global exploration and local exploitation ability of Particle Swarm Optimization(PSO) algorithm, an improved PSO algorithm integrated with Tabu Search(TS) is proposed. In PSO algorithm, optimal particle is perturbed with a linear increasing probability. When the global PSO algorithm converges to a certain degree, TS algorithm is used for local search of global optimum. Simulation results show that the convergence precision of the improved algorithm is very high, and can overcome premature convergence effectively.

Key words: Particle Swarm Optimization(PSO) algorithm, Tabu Search(TS), random perturbance, local optimum, convergence precision

中图分类号: 

• TP301.6