摘要
讨论了区间参数非线性规划问题.通过引入决策风险因子的概念,提出了一种不确定性非线性规划的一般命题形式.为求解该命题形式,提出一种自适应主从式并行遗传算法,该算法可以满足大规模优化问题的求解实时性要求,具有全局收敛性能.相对于常规主从式并行遗传算法,该算法通过动态调整从机的计算负荷,有效地解决了从机间计算负荷不均衡分布的问题.仿真结果表明了该自适应主从式并行遗传算法的可行性.*
This paper considers the nonlinear programming problem of interval parameters. A general interpretation formulation of nonlinear programming under uncertainty is proposed with the introd.uction of decision making risk factors. To solve this formulation, this paper presents a self-adaptive master-slave parallel genetic algorithm, which meets the real-time requirements of large scale optimization problem and has the capability of global convergence, Compared with the traditional master-slave parallel genetic algorithms, the presented algorithm can efficiently solve the problem of unbalanced distribution of computational load among the slave computers by dynamically adjusting computational load of the slave computers. Simulation result proves the feasibility of the presented algorithm.
出处
《信息与控制》
CSCD
北大核心
2006年第3期314-318,324,共6页
Information and Control
关键词
并行遗传算法
区间规划
非线性规划
决策风险因子
自适应
parallel genetic algorithm
interval programming
nonlinear programming
decision making risk factor
self-adaptive
