摘要
针对非线性优化问题讨论了一种基于迭代进程和适应值综合的自适应变异粒子群优化算法,该算法按照自适应变异方法从迭代进程上、以及从目标函数适应值上调整速度惯性因子,同时结合正态变异算子调整搜索方向。采用专用测试函数进行仿真测试分析,结果表明改进算法收敛,具有很高的搜索效率和求解精度。
A novel arithmetic was proposed based on particle swarm optimization （PSO）, which concentrated the advantages of the successive recurs＇ion process of adaptive mutation and the fitness of adaptive mutation. And the normal mutation arithmetic was also used to adjust the searching direction. Special functions were used to verify the stability and response speed of the arithmetic. The simulation results show that the nonlinear optimizing problem with the objective model functions can be solved with high searching efficiency and solution accuracy under the proposed method.
出处
《系统仿真学报》
EI
CAS
CSCD
北大核心
2007年第21期4922-4925,共4页
Journal of System Simulation
基金
湖南省自然科学基金(06JJ5112)
湘潭大学跨学科交叉项目(05IND04)
关键词
粒子群优化
自适应变异
正态变异
非线性优化问题
particle swarm optimization
adaptive mutation
normal mutation
nonlinear optimization problem
