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Most real w orld combinatorial optim ization problem s are difficult to solve w ith m ultiple objectives
w hich have to be optimized simultaneously. Over the last few years, researches have been proposed
several an t colony optim ization algorithm s to solve m ultiple objectives. The aim of this paper is to
review the recently proposed multi-objective an t colony optim ization (MOACO) algorithm s and compare
their perform ances on two, three and four objectives w ith different num bers of ants and num bers of
iterations. Moreover, a detailed analysis is perform ed for these MOACO algorithm s by applying them on
several m ulti-objective benchm ark instances of the traveling salesman problem. The results of the
analysis have show n th at m ost of the considered MOACO algorithm s obtained better perform ances for
m ore than tw o objectives and their perform ance depends slightly on the num ber of objectives, num ber
of iterations and num ber of ants used.
