Inferential Performance Assessment of Stochastic Optimisers and the Attainment Function
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
Design and analysis of stochastic local search for the multiobjective traveling salesman problem
Computers and Operations Research
A Review and Evaluation of Multiobjective Algorithms for the Flowshop Scheduling Problem
INFORMS Journal on Computing
Effective Hybrid Stochastic Local Search Algorithms for Biobjective Permutation Flowshop Scheduling
HM '09 Proceedings of the 6th International Workshop on Hybrid Metaheuristics
A two-phase local search for the biobjective traveling salesman problem
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
Improvement strategies for the F-Race algorithm: sampling design and iterative refinement
HM'07 Proceedings of the 4th international conference on Hybrid metaheuristics
Two-phase Pareto local search for the biobjective traveling salesman problem
Journal of Heuristics
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
A hybrid TP+PLS algorithm for bi-objective flow-shop scheduling problems
Computers and Operations Research
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
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Two-Phase Local Search (TPLS) is a general algorithmic framework for multi-objective optimization. TPLS transforms a multiobjective problem into a sequence of single-objective ones by means of weighted sum aggregations. This paper studies different sequences of weights for defining the aggregated problems for the bi-objective case. In particular, we propose two weight setting strategies that show better anytime search characteristics than the original weight setting strategy used in the TPLS algorithm.