Two-Phase Pareto Local Search to Solve the Biobjective Set Covering Problem
Thibaut Lust, Daniel Tuyttens
Abstract
Thibaut Lust, Daniel Tuyttens
Abstract
In this paper, we study the biobjective version of the set covering problem. To our knowledge, this problem has only been addressed in two papers before, and with heuristic methods. We propose a new heuristic, based on the two-phase Pareto local search, with the aim of generating a good approximation of the Pareto efficient solutions. In the first phase of this method, the supported efficient solutions or a good approximation of these solutions is generated. Then, a neighborhood embedded in the Pareto local search is applied to generate non-supported efficient solutions. In order to get high quality results, two elaborate local search techniques are considered: a very large-scale neighborhood search and a variable neighborhood search. We intensively study the parameters of these two techniques. We compare our results with state-of-the-art results and we show that with our method, better results are obtained for different indicators.
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In this paper, we study the biobjective version of the set covering problem. To our knowledge, this problem has only been addressed in two papers before, and with heuristic methods. We propose a new heuristic, based on the two-phase Pareto local search, with the aim of generating a good approximation of the Pareto efficient solutions. In the first phase of this method, the supported efficient solutions or a good approximation of these solutions is generated. Then, a neighborhood embedded in the Pareto local search is applied to generate non-supported efficient solutions. In order to get high quality results, two elaborate local search techniques are considered: a very large-scale neighborhood search and a variable neighborhood search. We intensively study the parameters of these two techniques. We compare our results with state-of-the-art results and we show that with our method, better results are obtained for different indicators.
Key concepts: Pareto principle, Heuristic, Local search (optimization), Mathematical optimization, Set (abstract data type), Computer science, Variable neighborhood search, Multi-objective optimization