中文摘要：

惩罚函数是遗传算法中解决非线性约束最优化问题最常用的方法之一.但传统的惩罚函数运用到遗传算法中往往难以控制惩罚因子,因此本文引进了一种结构简单、通用性强的新自适应惩罚函数,并证明了其收敛性.随后构建了基于新自适应惩罚函数的遗传算法,使得种群能快速进入可行域,并且提高了遗传算法的局部搜索能力.理论分析及仿真结果表明该算法具有参数少、稳定性强、收敛快等优点.

英文摘要：

Penalty function is one of the most commonly used method in genetic algorithm（GA） to solve nonlinear constraint optimization problems.For traditional penalty functions,it is always not easy to control penalty factors.In this paper we present a new adaptive penalty function with simpler construction and prove its convergence.Then based on this adaptive penalty function we present a new genetic algorithm,which can make populations quickly access to feasible regions and improve local search capacity of genetic algorithms.Theoretical analysis and simulation results show that this algorithm has stronger stability and better convergence but needs less parameters than other ones.