摘要
作者将非线性方程组的数值求解问题转化为线性约束最优化问题,然后利用遗传算法求解该最优化问题。为防止遗传算法过早收敛,作者将遗传算法改进为自适应并行遗传算法。数值模拟实验表明,该文的算法从另一个角度为求解非线性方程组提供了一条比较有效的途径。
In this paper the problem on numerical solution of nonliner equations is transformed into that of optimization with linear constraints, and then that solution is found through the genetic algorithm. We also revise genetic algorithm into self-adaptive parallel genetic algorithm to prevent premature convergence. Numerical simulation experiments show our algorithm offers an effective way to solve the nonlinear equations from another viewpoint.
出处
《华东师范大学学报（自然科学版）》
CAS
CSCD
北大核心
2004年第1期29-34,共6页
Journal of East China Normal University(Natural Science)
基金
上海市重点学科建设项目
关键词
非线性方程组
线性约束最优化问题
并行遗传算法
过早收敛
nonliner equations
optimization with linear constraints
parallel genetic algorithm
premature convergence
