基于约束处理和平滑技术的改进的进化算法

An Improved Genetic Algorithm Based on Constraint Handling and Smoothing Techniques

进化算法是解决复杂非线性规划的一种有效方法,然而其计算量通常比较大,约束较难处理。本文首先利用约束处理技术将约束最优化问题转化为无约束最优化问题以降低问题求解难度。其次,为了减少局部最优解的个数,利用了平滑技术,该技术可以消除不优于当前最优解的全部局部最优解。此外,设计新的交叉算子。基于此,本文提出一种改进的进化算法,实验结果表明该算法具有较低的计算量和更快的收敛速度。

Evolutionary algorithm is a new kind of efficient methods for complex nonlinear programming , however , the amount of their computation is usually very large , and the constraints can not be handled efficiently .In this paper, firstly, the constrained problem is transformed into an unconstrained one so as to reduce the difficulty of problem solving .Secondly , to reduce the number of local optimal solutions , a smoothing technique is adopted .It can eliminate all local optimal solutions which are not better than the current best solution found so far , and keep all the local optimal solutions which is better than the current best solution . Furthermore, a new crossover operator is designed .Based on all these, an improved evolutionary algorithm is proposed and experimental results show the efficiency of the proposed algorithm with less computation , higher convergent speed for all test problems .