Team Algorithms Based on Ant Colony Optimization --- A New Multi-Objective Optimization Approach

Authors:
Christian Lezcano;Diego Pinto;Benjamín Barán
Affiliations:
Polytechnical School, National University of Asunción, Paraguay;Polytechnical School, National University of Asunción, Paraguay;Polytechnical School, National University of Asunción, Paraguay
Venue:
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Year:
2008

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Abstract

This paper proposes a novel Team Algorithm (TA) approach based on Ant Colony Optimization (ACO) for multi-objective optimization problems. The proposed method has shown a significant cooperative effect of different 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 Assignment 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.