Simulated annealing ant colony algorithm for QAP

Abstract

A simulated annealing ant colony algorithm(ASAC) is presented to tackle the quadratic assignment problem (QAP). The simulated annealing method is introduced to the ant colony algorithm. The temperature which declines with the iterations is set in the algorithm. After each round of searching, the solution set got by the colony is treated as the candidate set. Base on the simulated annealing method, the solution in the candidate set is chosen to join the update set with possibility which determined by the temperature. The update set is used to update the trail information matrix. And also the current best solution is used to enhance the tail information. The pheromone trails matrix is reset when the algorithm is in the stagnant state. The computer experiments demonstrate this algorithm has high calculation stability and converging speed.