改进的蛙跳算法在多目标优化问题中的应用

Application of improved Shuffle Frog Leaping Algorithm to resolve multi-objective optimization problem

在介绍原始混洗蛙跳算法的基础上,引入遗传算法中的遗传算子,改进原始蛙跳算法的分组方法,提出一种改进的混洗蛙跳算法用于求解多目标优化问题。改进的算法以多目标0-1背包问题为例进行模拟实验,其实验结果表示,与原始的混洗蛙跳算法相比较,改进的蛙跳算法在求解多目标优化问题上具有更好的性能。

This paper draws genetic operators of GA and improves the method of SFLA group dividing based on introducing SFLA, puts forward an improved SFLA to resolve problem of multi-objective optimization. The improved method takes multi-objective 0-1 knapsack as an example for simulated experiment, which bears out that, compared with original SFLA, the improved SFLA has better performance on resolving improved SFLA problem.