Incorporation of Scalarizing Fitness Functions into Evolutionary Multiobjective Optimization Algorithms
Incorporation of Scalarizing Fitness Functions into Evolutionary Multiobjective Optimization Algorithms
复制标题
DOI：
10.1007/11844297_50
复制
发表时间：
2006-09
期刊：
影响因子：
--
通讯作者：
H. Ishibuchi;Tsutomu Doi;Y. Nojima
中科院分区：
文献类型：
--
作者：
H. Ishibuchi;Tsutomu Doi;Y. Nojima
本文提出了在进化多目标优化（EMO）算法中概率地使用标度适应度函数的思想。我们引入了两个概率来指定在EMO算法中使用标量适应度函数进行父代选择和代更新的频率。通过二目标、三目标和四目标的多目标0/1背包问题的计算实验，我们证明了概率化适应度函数的使用提高了EMO算法的性能。在特殊情况下，我们的想法可以看作是单目标进化算法（soea）中EMO方案的概率使用。从这个观点出发，我们来检验我们的想法的有效性。实验结果表明，该方法不仅提高了多目标问题的EMO算法的性能，而且提高了单目标问题的soea算法的性能。
This paper proposes an idea of probabilistically using a scalarizing fitness function in evolutionary multiobjective optimization (EMO) algorithms. We introduce two probabilities to specify how often the scalarizing fitness function is used for parent selection and generation update in EMO algorithms. Through computational experiments on multiobjective 0/1 knapsack problems with two, three and four objectives, we show that the probabilistic use of the scalarizing fitness function improves the performance of EMO algorithms. In a special case, our idea can be viewed as the probabilistic use of an EMO scheme in single-objective evolutionary algorithms (SOEAs). From this point of view, we examine the effectiveness of our idea. Experimental results show that our idea improves not only the performance of EMO algorithms for multiobjective problems but also that of SOEAs for single-objective problems.