Pareto local search algorithms for anytime bi-objective optimization

Authors:
Jérémie Dubois-Lacoste;Manuel López-Ibáñez;Thomas Stützle
Affiliations:
IRIDIA, CoDE, Université Libre de Bruxelles, Brussels, Belgium;IRIDIA, CoDE, Université Libre de Bruxelles, Brussels, Belgium;IRIDIA, CoDE, Université Libre de Bruxelles, Brussels, Belgium
Venue:
EvoCOP'12 Proceedings of the 12th European conference on Evolutionary Computation in Combinatorial Optimization
Year:
2012

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Abstract

Pareto local search (PLS) is an extension of iterative improvement methods for multi-objective combinatorial optimization problems and an important part of several state-of-the-art multi-objective optimizers. PLS stops when all neighbors of the solutions in its solution archive are dominated. If terminated before completion, it may produce a poor approximation to the Pareto front. This paper proposes variants of PLS that improve its anytime behavior, that is, they aim to maximize the quality of the Pareto front at each time step. Experimental results on the bi-objective traveling salesman problem show a large improvement of the proposed anytime PLS algorithm over the classical one.