Inferential Performance Assessment of Stochastic Optimisers and the Attainment Function
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
A discrete version of particle swarm optimization for flowshop scheduling problems
Computers and Operations Research
Computers and Operations Research
A Review and Evaluation of Multiobjective Algorithms for the Flowshop Scheduling Problem
INFORMS Journal on Computing
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
Performance assessment of multiobjective optimizers: an analysis and review
IEEE Transactions on Evolutionary Computation
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
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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This paper presents the steps followed in the design of hybrid stochastic local search algorithms for biobjective permutation flow shop scheduling problems. In particular, this paper tackles the three pairwise combinations of the objectives (i) makespan, (ii) the sum of the completion times of the jobs, and (iii) the weighted total tardiness of all jobs. The proposed algorithms are combinations of two local search methods: two-phase local search and Pareto local search. The design of the algorithms is based on a careful experimental analysis of crucial algorithmic components of the two search methods. The final results show that the newly developed algorithms reach very high performance: The solutions obtained frequently improve upon the best nondominated solutions previously known, while requiring much shorter computation times.