摘要
从蚁群算法中得到启示,将信息素的观点引入到求解组合优化问题的演化算法之中,提出了一种基因优化算法.该算法直接在基因的层面上进行优化,能学习劣解的基因,并用信息熵作为结束条件的判据.最后用该算法解决了两个典型的组合优化问题,取得了较好的结果.
The concept of pheromone in Ant Colony Optimization have introduced to Evolutionary Algorithm for combinatorial optimization problems, a Gene Optimized Algorithm (GOA) is presented. GOA optimizes directly at the gene level and can learn from the gene of bad individuals. The entropy is used for the terminal criterion of the algorithm. Two typical combinatorial problems is solved by GOA, experimental results have showed its efficiency.
出处
《武汉大学学报（理学版）》
CAS
CSCD
北大核心
2002年第3期315-318,共4页
Journal of Wuhan University:Natural Science Edition
基金
国家自然科学基金资助项目(60073043)
关键词
组合优化问题
演化算法
蚊群算法
信息素
熵
evolutionary algorithm
ant colory optimization
pheromone
entropy
