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Modified Discrete Particle Swarm Optimization Algorithm Based on Inver-Over Operator
Author(s): 
Pages: 97-102
Year: Issue:  1
Journal: Pattern Recognition and Artificial Intelligence

Keyword:  离散粒子群优化(DPSO) Inver-Over算子 郭涛算法 旅行商问题;
Abstract: 离散粒子群算法能充分利用粒子的局部极值和全局极值信息,但收敛速度慢、精度低;Inver-Over算子收敛速度快、精度高,但学习具有盲目性.结合二者优点,文中提出一种基于Inver-Over算子的改进离散粒子群优化算法.为防止早熟收敛,引入局部最优子群的概念,使粒子向局部最优子群中粒子学习而不是向个体局部最优学习.引入3个参数:学习选择概率用以确定粒子的学习对象,代数阈值确定何时向全局最优粒子学习,局部最优子群比决定最优子群的规模.讨论这些参数的选择原则,并给出相应参考选择范围.研究表明,文中算法与普通离散粒子群优化算法和郭涛算法相比,收敛速度和求解精度都有较大提高.
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