An adaptive improved ant colony algorithm based on information entropy was proposed for the disadvantages of low solution accuracy and easiness to fall into local optimum of ant colony algorithm.The population parameters were adaptively opti-mized by the algorithm's own characteristic definition combined with the entropy value.The pheromone updating strategy of group cooperation was used to expand the search range by guiding the whole population through more active individuals,the rela-tionship between convergence speed and search range was then balanced by rewarding the better path.When the population information entropy was too low,a local search strategy was added to further improve the accuracy of the algorithm.Experimen-tal results show that the improved algorithm has higher solution accuracy and the ability to jump out of the local optimum com-pared with the ant colony algorithm.
关键词
信息熵/蚁群算法/自适应/旅行商问题(TSP)/信息素/路径/局部搜索/种群
Key words
information entropy/ant colony algorithm/adaptive/travelling salesman problem(TSP)/information entropy/paths/local search/population
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