Performance of the MOSA Method for the Bicriteria Assignment Problem
Journal of Heuristics
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Design and analysis of stochastic local search for the multiobjective traveling salesman problem
Computers and Operations Research
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
A systems approach to evolutionary multiobjective structural optimization and beyond
IEEE Computational Intelligence Magazine
Two-phase Pareto local search for the biobjective traveling salesman problem
Journal of Heuristics
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
Two-phase multiobjective optimization
EC'05 Proceedings of the 6th WSEAS international conference on Evolutionary computing
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
An adaptive evolutionary multi-objective approach based on simulated annealing
Evolutionary Computation
Finding pareto-optimal set by merging attractors for a bi-objective traveling salesmen problem
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
Expert Systems with Applications: An International Journal
A co-evolutionary multi-objective optimization algorithm based on direction vectors
Information Sciences: an International Journal
Computers & Mathematics with Applications
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This article proposes the Two-Phase Local Search for finding a good approximate set of non-dominated solutions. The two phases of this procedure are to (i) generate an initial solution by optimizing only one single objective, and then (ii) to start from this solution a search for non-dominated solutions exploiting a sequence of different formulations of the problem based on aggregations of the objectives. This second phase is a single chain, using the local optimum obtained in the previous formulation as a starting solution to solve the next formulation. Based on this basic idea, we propose some further improvements and report computational results on several instances of the biobjective TSP that show competitive results with state-of-the-art algorithms for this problem.