区间自适应遗传算法优化无约束非线性规划问题

Optimization of Unconstrained Nonlinear Programming Problems with Interval Adaptive Genetic Algorithms

针对无约束非线性规划传统优化方法存在的问题,将区间自适应遗传算法引入无约束非线性规划优化中,算法可以利用当前进化信息,自适应移动搜索区间,找到全局最优解,故可缩短搜索区间长度,提高编码精度,降低算法计算量,解决了传统遗传算法处理优化问题时,给定区间必须包含最优解这一问题,这也是本算法有别于其他优化算法的独特优势,为某些最优解所在区间难以估计的无约束非线性规划问题的优化提供了一条有效可行的途径.系统阐述了区间自适应遗传算法的原理,给出了算法优化无约束非线性规划问题的步骤,以MatlabR2016b仿真方式对算法进行了实例测试,结果表明,方法是一种计算稳定、正确、有效、可靠实用的无约束非线性规划优化方法.

To solve the problems existing in the traditional unconstrained nonlinear programming optimization method,this paper introduces interval adaptive genetic algorithm into optimizing unconstrained nonlinear programming problems.The algorithm can use the current evolution information to adaptively move the search interval,find the global optimal solution.So it can shorten the search interval length,improve the coding accuracy,reduce the amount of calculation,and it solve the problem in traditional genetic algorithm dealing with optimization problems that the given interval must contain the optimal solution.This is also the unique advantage of this algorithm compared to other optimization algorithms.It provides an effective and feasible way for some unconstrained nonlinear programming problems that the interval where the optimal solution lies in is difficult to estimate.In this paper,the principle of interval adaptive genetic algorithm is expounded systematically,and the steps of the algorithm to optimize the unconstrained nonlinear programming problem are given.The algorithm is tested by Matlab simulation.The results show that the method is a computationally stable,accurate,efficient,reliable and practical optimization method for the unconstrained nonlinear programming.