G. Rudolph et al. (Eds.): PPSN X, LNCS 5199, pp. 773–783, 2008. © Springer-Verlag Berlin Heidelberg 2008 Team Algorithms Based on Ant Colony Optimization – A New Multi-Objective Optimization Approach Christian Lezcano, Diego Pinto, and Benjamín Barán Polytechnical School - National University of Asunción P.O. Box 2111 - Paraguay {clezcano,dpinto,bbaran}@pol.una.py http://www.una.py Abstract. This paper proposes a novel Team Algorithm (TA) approach based on Ant Colony Optimization (ACO) for multi-objective optimization prob- lems. The proposed method has shown a significant cooperative effect of dif- ferent algorithms combined in a team of algorithms, achieving robustness in the resolution of a set of various combinatorial problems. Experimentally, the proposed approach has verified a balance on different performance measures in problems as the Traveling Salesman Problem (TSP), the Quadratic Assign- ment Problem (QAP) and the Vehicle Routing Problem with Time Windows (VRPTW). Robustness and balance are achieved due to a novel classification and selection of the algorithms to be used by the team, considering Pareto concept. Keywords: Team Algorithms (TA), Ant Colony Optimization (ACO) and Multi-objective Optimization Problem (MOP). 1 Introduction A Multi-objective Optimization Problem (MOP) can be defined as the problem of finding a set of solutions that satisfies given constraints and optimizes several objec- tive functions simultaneously. Usually, these objective functions are in conflict with each other [1]. MOP is widely treated with different optimization paradigms [1-4], as Multi-Objective Evolutionary Algorithm – MOEA, which have been successfully applied to solve highly complex problems. On the other hand, the "No Free Lunch - NFL" theorem [5] states that on average, all algorithms have the same performance. Considering the NFL theorem, the development of Team Algorithms (TA) [6] is a valid alternative for achieving high robustness on average. This paper proposes a novel TA approach based on Multi-Objective Ant Colony Optimization (MOACO) algorithms for solving combinatorial optimization problems in a multi-objective context. Basically, the new proposal tries to improve the state of the art in this area [6]. Experimental tests were carried out on different bi-objective instances of the Traveling Salesman Problem (TSP), the Quadratic Assignment Prob- lem (QAP) and the Vehicle Routing Problem with Time Windows (VRPTW). To