Adaptive ant colony optimization algorithms and its convergence
一类自适应蚁群算法及其收敛性分析

A class of adaptive ant colony optimization(ACO) algorithms is presented to avoid the deficiency of typical ACO that often runs into local optimum.Global searching and convergence abilities are improved by adaptively changing the pheromone trails evaporation parameters.Some convergence properties for the algorithms are analyzed with the Markov process approach.Further more,an algorithm with guaranteed convergence to the optimal solution is developed.The simulation results for typical TSP problems demonstrate that the proposed algorithms are more effective than those for other modified ant systems.