基于遗传算法的数值优化约束问题的研究

针对数值优化约束中出现的大规模、多峰多态函数,含离散变量等情况下的全局优化问题,采用常规的优化方法,收敛速度较慢,求得全局极值的概率较低.提出用遗传算法的数值优化约束问题解决,通过数值仿真实验结果表明,该算法性能优于现有其它算法,它不仅可以处理线性等式约束,而且还可以处理非线性等式约束,同时提高了收敛速度和解的精度,是高效稳健的智能算法,具有很高的全局寻优能力和很快的收敛速度,对求解复杂多峰多态函数的优化约束问题具有可行性和有效性.

Study of Numerical Optimization Constraint Problems Based on Genetic Algorithm

For numerical optimization constraints appear in the large-scale, multi-function polymorphism, on global opti-mization with discrete variables under such circumstances, General optimization method, convergence is slow to seek global extremum of low probability. Made with genetic algorithm of numerical optimization constraints problem solution, by numerical simulation experimental results indicates that, the algorithm performance better than existing other algorithm, it not only can processing linear equation constraints, and also can processing nonlinear equation constraints, while improve has convergence speed reconciliation of precision, is efficient sound of intelligent algorithm, has is high of global found excellent ability and soon of convergence speed, on solution complex more peak more State function of optimization constraints problem has feasibility and effectiveness.