Open AccessAnt colony algorithm based on data classification
Author(s) -
Wanrong Tan,
Jian Gao,
Junhui Rao
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/768/7/072099
Subject(s) - ant colony optimization algorithms , travelling salesman problem , degree (music) , convergence (economics) , computer science , heuristic , class (philosophy) , mathematical optimization , ant colony , scale (ratio) , algorithm , local optimum , set (abstract data type) , artificial intelligence , data mining , mathematics , quantum mechanics , acoustics , physics , economics , programming language , economic growth
Aiming at the drawbacks of ant colony algorithm applied in the medium and large scale traveling salesman problem (TSP), this paper proposes an ant colony algorithm based on data classification (DACO),in which data classification method takes attraction degree as an important consideration.First,the initial data of the selected data set is divided into two categories - candidate class and elimination class according to the law of attraction degree.Then the elimination class is eliminated to reduce the search time.At the same time, the attraction degree can effectively guide the algorithm to search for the optimal solution when the previous pheromone heuristic effect is not significant.In addition, 3-opt local search algorithm is added to further optimize the solution.The experimental results show that the DACO has a better convergence and solution quality compared with other ant colony algorithms.