An analysis of local search for the bi-objective bidimensional knapsack problem

Authors:
Leonardo C. T. Bezerra;Manuel López-Ibáñez;Thomas Stützle
Affiliations:
IRIDIA, Université Libre de Bruxelles, Brussels, Belgium;IRIDIA, Université Libre de Bruxelles, Brussels, Belgium;IRIDIA, Université Libre de Bruxelles, Brussels, Belgium
Venue:
EvoCOP'13 Proceedings of the 13th European conference on Evolutionary Computation in Combinatorial Optimization
Year:
2013

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Abstract

Local search techniques are increasingly often used in multi-objective combinatorial optimization due to their ability to improve the performance of metaheuristics. The efficiency of multi-objective local search techniques heavily depends on factors such as (i) neighborhood operators, (ii) pivoting rules and (iii) bias towards good regions of the objective space. In this work, we conduct an extensive experimental campaign to analyze such factors in a Pareto local search (PLS) algorithm for the bi-objective bidimensional knapsack problem (bBKP). In the first set of experiments, we investigate PLS as a stand-alone algorithm, starting from random and greedy solutions. In the second set, we analyze PLS as a post-optimization procedure.