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A heuristic algorithm for mean flowtime objective in flowshop scheduling
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AE '97 Selected Papers from the Third European Conference on Artificial Evolution
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Stochastic Local Search: Foundations & Applications
Stochastic Local Search: Foundations & Applications
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Computers and Operations Research
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Effective Hybrid Stochastic Local Search Algorithms for Biobjective Permutation Flowshop Scheduling
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HM'07 Proceedings of the 4th international conference on Hybrid metaheuristics
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Journal of Heuristics
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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
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IEEE Transactions on Evolutionary Computation
Performance assessment of multiobjective optimizers: an analysis and review
IEEE Transactions on Evolutionary Computation
Reducing the run-time complexity of multiobjective EAs: The NSGA-II and other algorithms
IEEE Transactions on Evolutionary Computation
A hybrid TP+PLS algorithm for bi-objective flow-shop scheduling problems
Computers and Operations Research
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Journal of Systems and Software
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
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LION'12 Proceedings of the 6th international conference on Learning and Intelligent Optimization
An analysis of local search for the bi-objective bidimensional knapsack problem
EvoCOP'13 Proceedings of the 13th European conference on Evolutionary Computation in Combinatorial Optimization
On local search for bi-objective knapsack problems
Evolutionary Computation
Computational Statistics & Data Analysis
Region based memetic algorithm for real-parameter optimisation
Information Sciences: an International Journal
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This paper presents a new, carefully designed algorithm for five bi-objective permutation flow shop scheduling problems that arise from the pairwise combinations of the objectives (i) makespan, (ii) the sum of the completion times of the jobs, and (iii) both, the weighted and non-weighted total tardiness of all jobs. The proposed algorithm combines two search methods, two-phase local search and Pareto local search, which are representative of two different, but complementary, paradigms for multi-objective optimization in terms of Pareto-optimality. The design of the hybrid algorithm is based on a careful experimental analysis of crucial algorithmic components of these two search methods. We compared our algorithm to the two best algorithms identified, among a set of 23 candidate algorithms, in a recent review of the bi-objective permutation flow-shop scheduling problem. We have reimplemented carefully these two algorithms in order to assess the quality of our algorithm. The experimental comparison in this paper shows that the proposed algorithm obtains results that often dominate the output of the two best algorithms from the literature. Therefore, our analysis shows without ambiguity that the proposed algorithm is a new state-of-the-art algorithm for the bi-objective permutation flow-shop problems studied in this paper.