Why Quality Assessment Of Multiobjective Optimizers Is Difficult
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Chained Lin-Kernighan for Large Traveling Salesman Problems
INFORMS Journal on Computing
Multicriteria Optimization
Bound sets for biobjective combinatorial optimization problems
Computers and Operations Research
A two-phase local search for the biobjective traveling salesman problem
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
Performance assessment of multiobjective optimizers: an analysis and review
IEEE Transactions on Evolutionary Computation
Speed-up techniques for solving large-scale biobjective TSP
Computers and Operations Research
Effective Hybrid Stochastic Local Search Algorithms for Biobjective Permutation Flowshop Scheduling
HM '09 Proceedings of the 6th International Workshop on Hybrid Metaheuristics
Adaptive "Anytime" two-phase local search
LION'10 Proceedings of the 4th international conference on Learning and intelligent optimization
A hybrid TP+PLS algorithm for bi-objective flow-shop scheduling problems
Computers and Operations Research
Very large-scale neighborhood search for solving multiobjective combinatorial optimization problems
EMO'11 Proceedings of the 6th international conference on Evolutionary multi-criterion optimization
Automatic configuration of state-of-the-art multi-objective optimizers using the TP+PLS framework
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Improving the anytime behavior of two-phase local search
Annals of Mathematics and Artificial Intelligence
Pareto local search algorithms for anytime bi-objective optimization
EvoCOP'12 Proceedings of the 12th European conference on Evolutionary Computation in Combinatorial Optimization
On local search for bi-objective knapsack problems
Evolutionary Computation
Computational Statistics & Data Analysis
Variable and large neighborhood search to solve the multiobjective set covering problem
Journal of Heuristics
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In this work, we present a method, called Two-Phase Pareto Local Search, to find a good approximation of the efficient set of the biobjective traveling salesman problem. In the first phase of the method, an initial population composed of a good approximation of the extreme supported efficient solutions is generated. We use as second phase a Pareto Local Search method applied to each solution of the initial population. We show that using the combination of these two techniques: good initial population generation plus Pareto Local Search gives better results than state-of-the-art algorithms. Two other points are introduced: the notion of ideal set and a simple way to produce near-efficient solutions of multiobjective problems, by using an efficient single-objective solver with a data perturbation technique.