GA-PSO混合规划算法

A new programming of mixed genetic algorithm with particle swarm optimization

目的 提出一个比GP算法更优的GA-PSO混合的规划算法。方法 通过将层次型问题的描述转换为固定长度线形结构的描述方式,使GP算法与GA规划算法达到统一;通过构造运算符,将PSO算法引入到GA规划算法框架之中,形成GA-PSO混合规划算法。结果 从解的描述、遗传算子、PSO运算符的构造再到GA-PSO算法框架,提出了完整的GA-PSO混合规划算法。结论 实证研究显示,GA-PSO混合规划算法优于GP算法以及GA算法。

Aim GA-PSO, an optimize algorithm which is superior to pure GP is given.Methods Through changing the question′s description of hierarchy into the fix length linear structure, making GP and GA come to an unification. In addition, a new operator is introduced and applied to add PSO into GA frame, which forms the GA-PSO programming.Results From the description of the solving to the genetic operators, PSO operators and the construction of GA-PSO frame, a integrated GA-PSO programming is presented.Conclusion GA-PSO optimize algorithm is superior to pure GP and GA. It is a good optimization method with strong competitiveness.