CAJ | 학술논문

提出了一种改进的差异演化算法MDE(modified differential evolution algorithm),该算法首先对差异演化算法的缩放因子进行混沌计算,减少了用户参与程度,平衡了算法的收敛速度与全局搜索能力;其次引入灾变因子,对群体中的个体进行小概率淘汰,同时又有新的个体加入,从而提高了群体多样性,提高了算法的全局搜索能力.仿真实验与工程实例表明,该算法具有较好的全局搜索能力.
제출료일충개진적차이연화산법MDE(modified differential evolution algorithm),해산법수선대차이연화산법적축방인자진행혼돈계산,감소료용호삼여정도,평형료산법적수렴속도여전국수색능력;기차인입재변인자,대군체중적개체진행소개솔도태,동시우유신적개체가입,종이제고료군체다양성,제고료산법적전국수색능력.방진실험여공정실례표명,해산법구유교호적전국수색능력.
Differential evolution (DE) is one kind of evolution algorithm based on difference of individuals. DE has exhibited good performance on optimization. However, for the high dimension and perplexed function,the algorithm is apt to fall into premature convergence, its performance is strongly influenced by the value of each strategy parameter including scale factor. Therefore, a modified differential evolution algorithm (MDE) was proposed to solve the optimization problems. First, the scale factor was randomly initialized and calculated by chaos each generation, which decreases the participation of user and balances the convergency speed and global optimal capability. Next, disaster factor was introduced to eliminate the individual of a small probability, along with a new individual generating, which can increase the diversity of population and global optimal capability. Simulated results and engineering optimization design example showed that MDE outperforms standard DE in global optimal capability.