This paper discusses simple local search approaches for approximating the efficient set of multiobjective combinatorial optimization problems. We focus on algorithms defined by a neighborhood structure and a dominance relation that iteratively improve an archive of nondominated solutions. Such methods are referred to as dominance-based multiobjective local search. We first provide a concise overview of existing algorithms, and we propose a model trying to unify them through a fine-grained decomposition. The main problem-independent search components of dominance relation, solution selection, neighborhood exploration and archiving are largely discussed. Then, a number of state-of-the-art and original strategies are experimented on solving a permutation flowshop scheduling problem and a traveling salesman problem, both on a two- and a three-objective formulation. Experimental results and a statistical comparison are reported in the paper, and some directions for future research are highlighted. © Springer Science+Business Media, LLC 2011.
CITATION STYLE
Liefooghe, A., Humeau, J., Mesmoudi, S., Jourdan, L., & Talbi, E. G. (2012, April). On dominance-based multiobjective local search: Design, implementation and experimental analysis on scheduling and traveling salesman problems. Journal of Heuristics. https://doi.org/10.1007/s10732-011-9181-3
Mendeley helps you to discover research relevant for your work.