欢迎您！东篱公司
退出
-
申报数据库
-
立项数据库
-
成果数据库
-
项目获奖数据库
中文摘要：
提出了一种基于双种群的动态交换策略的粒子群优化算法。该算法将初始种群划分为两个子群P1和P2，而P1和P2遵循不同的寻优机制，然后通过对个体极值（pBest）和全局极值（gBest）的选取进行调整，并在迭代过程中动态的交换两个子群的个体，从而能够更好的完成多目标优化算法对于Pareto front的搜索和逼近。通过对标准测试函数的实验，证明了该算法的可行性和有效性。
英文摘要：
A new particle swarm optimization algorithm based on double group＇s dynamic swap strategy is proposed. The algorithm divides the original group into two parts： P1 and P2 which conform to different mechanism for searching, and is beneficial to search and approach to Pareto front by adjusting the pBest and gBest, and exchanging dynamically in the process of iteration. Through the experiments of standard test functions, the feasibility and validity of this approach are proved.
同期刊论文项目
同项目期刊论文
A general projection gradient method for linear constrained optimization with superlinear convergenc
A Superlinearly Convergent SQP Algorithm for Mathematical Programs
with Linear Complementarity Const
A globally and superlinearly convergent smoothing Broyden-like method for solving nonlinear compleme
An interior point type QP-free algorithm with superlinear convergence for inequality constrained opt
A superlinearly convergent SSLE algorithm for optimization problems with linear complementarity cons
A Sequential Equality Constrained Quadratic Programming Algorithm
for Inequality Constrained Optimiz
A superlinearly convergent SQP algorithm for mathematical programs with linear complementarity const
期刊信息
位置：