摘要
针对可靠性冗余优化问题中解的精度低及算法早熟收敛的问题,提出一种自适应的差分进化算法.该算法在原始差分进化算法的基础上修改了变异算子和交叉算子;在进化过程中,缩放因子F和交叉概率CR分别由三角函数实现自适应调节,以提高可行解的多样性及算法的收敛速度.解决了可靠性冗余优化问题解的精度低及早熟收敛问题.实验结果表明,该算法在解决可靠性冗余优化问题上不仅提高了解的精度,且具有更好的稳定性及更快的收敛速度.
Aimming at low accuracy solutions and the premature convergence problem in the reliability redundancy optimization problems,we proposed an adaptive differential evolution algorithm,which modified mutation operator and crossover operator on the basis of the original differential evolution algorithm.In the process of evolution,the scale factor Fand crossover probability CR were adaptively adjusted by trigonometric function respectively to improve the diversity of the feasible solution and convergence rate of the algorithm.It solved the low accuracy solutions and premature convergence problems of the reliability redundancy optimization problems.Experimental results show that the algorithm not only improves the accuracy of solution,but also has better stability and faster convergence rate for solving the reliability redundancy optimization problem.
出处
《吉林大学学报（理学版）》
CAS
CSCD
北大核心
2016年第1期70-76,共7页
Journal of Jilin University:Science Edition
基金
教育部"长江学者和创新团队发展计划"创新团队项目(批准号:IRT1017)
吉林省教育厅科学技术研究项目(批准号:[2013]461)
吉林省科技厅科技发展计划项目(批准号:20140204048GX)
关键词
非线性规划
自适应差分进化
可靠性优化
冗余分配
约束优化
nonlinear programming
adaptive differential evolution
reliability optimization
redundancy allocation
constrained optimization
