摘要
针对实际机械优化设计中大量的非线性规划问题,提出一种改进的遗传算法.在对单纯形搜索与算术交叉思想进行分析的基础上,将二者相结合,提出了改进的交叉算子以提高遗传算法的局部寻优能力,将种群逐步向极值点引导,实现算法的快速寻优.同时,为了更好地引导非可行个体趋近可行域,改善解的可行性,将惩罚策略与修复策略相结合提出修复算子,对不可行解进行修复操作,加快个体趋近可行域的速度,提高算法搜索效率以及对非线性约束的处理能力,从而达到改善算法整体性能的目的.实际机械工程优化设计问题的应用研究验证了这种方法的有效性.
An improved genetic algorithm is proposed according to lots of nonlinear programming problems found in actual mechanical design optimization. Based on the analyses of simplex search and arithmetic crossover and combining both together, an improved crossover operator is presented to improve the local searching capability of genetic algorithm and lead gradually the population to the extreme point so as to implement the rapid ,searching. At the same time, to lead infeasible individuals to approach the feasible region so as to improve their feasibilities for the better, the penalty and repair strategies are associated with each other to form a repair operator for repairing infeasible individuals, accelerating the speed of the individuals to approach the feasible region and improving the searching efficiency and the capability in solving the nonlinear constraint. As a whole, the performance of the algorithm is therefore improved. The validity of the algorithm proposed is verified by actual applications to nonlinear programming problems.
出处
《东北大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2005年第12期1123-1126,共4页
Journal of Northeastern University(Natural Science)
基金
国家自然科学基金资助项目(60374003)
国家重点基础研究发展计划项目(2002CB312200)
教育部暨辽宁省流程工业综合自动化重点实验室开放课题基金资助项目
关键词
遗传算法
非线性规划
单纯形搜索
交叉算子
惩罚策略
修复算子
genetic algorithm
nonlinear programming
simplex search
crossover operator
penalty strategy
repair operator
