【作者】 李彦苍； 索娟娟；

【Author】 LI Yan-cang, SUO Juan-juan,(Architectural Engineering Department of Hebei Engineering Institute, Handan 056038; Hebei University, Baoding 071002)

【机构】 河北工程学院土木建筑工程系； 河北大学河北；

【摘要】 传统遗传算法(GA)存在着易陷入局部最优的缺陷,本文提出了一种先利用信息熵调整遗传与变异的侧重点,实现算法参数自适应调节,而后再利用蚁群算法中信息素反馈的思想,在基因层面上对GA进行优化以确定最优解的改进的遗传算法.通过一典型的投资组合优化问题的解决,证明了该法具有较高的稳定性和鲁棒性.更多还原

【Abstract】 An adaptive GA based on entropy and pheromone is introduced in the paper . The entropy is used to give the scare of the possible optimized zone and to control the selection and crossover, and the gene optimized algorithm based on the concept of the pheromone in ACO is used to find the result. Experiment results have shown its efficiency in solving the combinatorial optimization problems.更多还原