Very large-scale neighborhood search for solving multiobjective combinatorial optimization problems

Authors:
Thibaut Lust;Jacques Teghem;Daniel Tuyttens
Affiliations:
Faculté Polytechnique de Mons, Laboratory of Mathematics & Operational Research, Mons, Belgium;Faculté Polytechnique de Mons, Laboratory of Mathematics & Operational Research, Mons, Belgium;Faculté Polytechnique de Mons, Laboratory of Mathematics & Operational Research, Mons, Belgium
Venue:
EMO'11 Proceedings of the 6th international conference on Evolutionary multi-criterion optimization
Year:
2011

Citing 10
Cited 0

Multi-Objective Optimization Using Evolutionary Algorithms

Multi-Objective Optimization Using Evolutionary Algorithms
Evolutionary Algorithms for Solving Multi-Objective Problems

Evolutionary Algorithms for Solving Multi-Objective Problems
Genetic Algorithms for the 0/1 Knapsack Problem

ISMIS '94 Proceedings of the 8th International Symposium on Methodologies for Intelligent Systems
A survey of very large-scale neighborhood search techniques

Discrete Applied Mathematics
A GRASP Algorithm for the Multi-Objective Knapsack Problem

QEST '04 Proceedings of the The Quantitative Evaluation of Systems, First International Conference
MOTGA: A multiobjective Tchebycheff based genetic algorithm for the multidimensional knapsack problem

Computers and Operations Research
Two-phase Pareto local search for the biobjective traveling salesman problem

Journal of Heuristics
A multi-objective genetic local search algorithm and itsapplication to flowshop scheduling

IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach

IEEE Transactions on Evolutionary Computation
On the performance of multiple-objective genetic local search onthe 0/1 knapsack problem - a comparative experiment

IEEE Transactions on Evolutionary Computation

Quantified Score

Hi-index	0.00

Visualization

Abstract

Very large-scale neighborhood search (VLSNS) is a technique intensively used in single-objective optimization. However, there is almost no study of VLSNS for multiobjective optimization. We show in this paper that this technique is very efficient for the resolution of multiobjective combinatorial optimization problems. Two problems are considered: the multiobjective multidimensional knapsack problem and the multiobjective set covering problem. VLSNS are proposed for these two problems and are integrated into the two-phase Pareto local search. The results obtained on biobjective instances outperform the state-of-the-art results for various indicators.