well, you need multi objective ant colony algorithm or other types of multi objective evolutionary algorithms to tackle MO problems. just google the terms "multi objective ant colony" and you will get lots of useful links, however, following papers might help

http://dl.acm.org/citation.cfm?id=1645454.1645819&coll=DL&dl=GUIDE&CFID=350439879&CFTOKEN=36315539

http://dl.acm.org/citation.cfm?id=1389095.1389101&coll=DL&dl=GUIDE&CFID=350439879&CFTOKEN=36315539

http://dl.acm.org/citation.cfm?id=1848648.1849083&coll=DL&dl=GUIDE&CFID=350439879&CFTOKEN=36315539

Thanks

Most of studies about metaheuristics for multi-objective optimization are focused on population-based metaheuristics, such as evolutionary algorithms. When adapting a single-objective algorithm into multi-objective, one of the most important issues is how to adapt the selection operator. Comparing solutions in single-objective problems is simple, but in multi-objective problems the concept of dominance is usually used (it is said that one solution dominates another if it is as good or better on every objective).

Since in multi-objective optimization the main goal is to find multiple and diverse solutions in the Pareto front, the use of explicit diversification techniques such as niching methods (e.g. sharing, crowding and clustering) is also more common in multi-objective than single-objective optimization.

I believe all evolutionary algorithms can solve the multiobjective problems.

Once you know how to connect the decision variables (i.e. x1, x2, ... xn) in the decision space to the objective space (i.e. z1, z2, ... zn), you will able to build your own multiobjective algorithm using whatever method you like (please see the attached figure for the illustration). 

Then, you might choose one of many methods for collecting a set of optimal solutions in the objective space, so-called pareto front (i.e. non dominated sorting methods).

Reading some multiobjective optimization books might be enhance your understanding. One of my favorit book is K.Deb's Multi-objective Optimization using Evolutionary Algorithms ( http://www.amazon.com/Multi-Objective-Optimization-Using-Evolutionary-Algorithms/dp/047187339X ). You might check this book in your library. He also published some of MO codes in his website ( http://www.iitk.ac.in/kangal/codes.shtml )

http://www.amazon.com/Multi-Objective-Optimization-Using-Evolutionary-Algorithms/dp/047187339X

http://www.iitk.ac.in/kangal/codes.shtml

I am agree with Doddy Prayogo. NSGAII is the most common method applied in this area. Some researchers, at first, convert the multi-objective problem to single objective one and then solve it. However, this approach can not find all pareto-optimal solutions.