摘要
遗传算法具有快速全局搜索能力,但对于系统中的反馈信息却没有利用,往往导致无为的冗余迭代,求解效率不高。而蚁群算法是通过信息素的累积和更新来收敛于最优路径,具有分布、并行、全局收敛能力,但是搜索初期信息素匮乏,导致算法速度慢。通过将两种算法进行融合,克服两种算法各自的缺陷,优势互补,形成一种时间效率和求解效率都比较好的启发式算法。并通过仿真计算,表明融合算法的性能优于遗传算法和蚁群算法。
Genetic algorithm has the ability of doing a global searching quickly and stochastically,but it can’t make use of enough system feedback information. It often has to do a large redundancy repeat for the result. So the efficiency to solve results is reduced. Ant colony optimization converges on the optimization path through information pheromone accumulation and renewal. It has the ability of parallel processing and global searching. Because there is little information pheromone on the path early,the speed at which the ant colony optimization gives the solution is slow. A new algorithm has been put forward,it utilizes the advantages of the two algorithms and overcomes their disadvantages. Experimental results from the simulation show the algorithm excels genetic algorithm and ant colony optimization in performance.
出处
《科学技术与工程》
2010年第16期4017-4020,共4页
Science Technology and Engineering
基金
国家重点实验室开放基金(KF09091)资助
关键词
遗传算法
蚁群算法
融合
优化
genetic algorithm ant colony optimization combination optimize
