摘要
1引言
工程、数学等领域经常遇到大量的约束优化(或非线性规划)问题,需要对约束条件进行处理.目前,还没有一种通用的传统优化方法,能够处理各种类型的约束.相比,遗传算法(GA)在这一领域,比其它方法更有巨大优势和应用潜力.遗传算法的群体搜索策略和不依赖梯度信息的计算方式,使得它在处理约束优化问题时比传统搜索算法通用和有效[1].
In all aspects of GA,including the constraints-handling technique,encoding,fitness function and genetic operator, etc, G A is able to provide an alternative optimization technique for constrained problems that are solved in a wide variety of domains. The penalty one is the most valid and flexible in the all techniques. The fundamental methods that can solve the nonlinear programming problems in the GA area are reviewed and discussed in this paper.
出处
《计算机科学》
CSCD
北大核心
2002年第6期98-101,共4页
Computer Science
关键词
约束优化问题
遗传算法
随机算法
群体搜索策赂
Genetic algorithms,Constraints-handling techniques,Nonlinear programming
