摘要
设计了一种引入了模拟退火机制的并行粒子群算法.该算法结合了基本粒子群优化算法的快速寻优能力和模拟退火算法的概率突跳性,避免了基本粒子群优化算法易于陷入局部最优的缺点,提高了进化后期算法的收敛精度.将该算法用于解决车辆路径问题,实验结果表明该算法具有较好的性能.
The proposed parallel particle swarm optimization （PSO） algorithm combines the fast optimum search ablity of original PSO with probability jump property of simulated annealing （SA）. It can avoid trapping to local minima as compared with original PSO and improve the accuracy in the later evolution period. The proposed algorithm was applied to the vehicle routing problem. The experiment results verify that the new algorithm is effective.
出处
《上海理工大学学报》
EI
CAS
北大核心
2007年第5期435-439,444,共6页
Journal of University of Shanghai For Science and Technology
基金
上海市高校选拔培养优秀青年教师科研专项基金资助项目(29-017-2)
上海市重点学科建设资助项目(T0502)
关键词
并行粒子群算法
模拟退火机制
车辆路径问题
parallel particle swarm optimization
simulated annealing mechanism
vehicle routing problem
