Binary Ant Colony Algorithm with Scouting Subgroup for Function Optimization Problem
To solve function optimization problem,this work introduces binary code of genetic algorithm to standard ant colony algorithm.But this algorithm often gets stuck into premature stagnation during the iteration process.Therefore,this paper proposes an improved algorithm with a scouting subgroup at a certain ratio.It can expand the searching space for solution by random search-route of scouting subgroup.And hybrid pheromone's updating strategy is adopted to make use of both current-fruit and precedent-fruit.The pheromone of the best-scouting subgroup's route as well as others' is increased.At last,the improved algorithm was tested for a series of classical functions.The results show that it can not only handle these optimization problems effectively,but also can avoid pre-maturity and accelerate constringency.