1（School of Management,Hefei University of Technology,Hefei 230009,China）2（Institute of Information Technology & Engineering Management,Tongling College,Tongling 244000,China）
Abstract：For the low performance of interactive genetic algorithm due to the small population size,less evolution generation and heavy evaluation burden,an interactive ant colony genetic algorithm （iACGA） using elitist strategy is proposed,and ant colony optimization （ACO） is introduced in genetic manipulation.From the perspective of human-computer interaction and reducing the burden of user evaluation,the algorithm structure of iACGA is designed.Firstly,the solutions are structured according to the ACO pheromone matrix,and selection operator in genetic algorithm is replaced by ACO pheromone.After the implementation of crossover and mutation operation,the system output of the solutions are displaying to human.iACGA User only needs to point out one optimal solution in current generation of history without specific fitness of each individual,and pheromone is updated accordingly.The experiments of function optimization and car styling design show that the algorithm has higher performance,and can effectively reduce user fatigue.
2016, Vol. 37 
