Abstract: We proposed an improved ant colony optimization algorithm to solve the problem that the structure of proposed algorithms are complicated, parameters of the algorithms are depended on and effect of the algorithms is not well about the single row facility layout problem. A strategy based on the adaptive classification of objective function value was designed to implement the survival of the fittest for pheromone increment, improving the pheromone updating rule. By simplifying the transition probability, the calculation complexity and dependence on the parameters were reduced. Elite candidates were introduced to increase the probability of selecting good facilities. And a hill climbing method based on insertion neighborhood structure was introduced into the ant algorithm for local optimization. The simulation results show that as to the total average time of 28 large instances, the proposed algorithm is just 14% of Hybrid Genetic Algorithm, 5% of Lin-Kernighan Heuristic and 50% of Scatter Search Algorithm. So the algorithm can converge to optimal solutions speedily and stably. The performance of the proposed algorithm is better than other algorithms.

Key words: single row facility layout problem, ant colony optimization, hill climbing method, local search