A fitness landscape analysis of Pareto local search on bi-objective permutation flowshop scheduling problems

Abstract : We study the difficulty of solving different bi-objective formulations of the permutation flowshop scheduling problem by adopting a fitness landscape analysis perspective. Our main goal is to shed the light on how different problem features can impact the performance of Pareto local search algorithms. Specifically, we conduct an empirical analysis addressing the challenging question of quantifying the individual effect and the joint impact of different problem features on the success rate of the considered approaches. Our findings support that multi-objective fitness landscapes enable to devise sound general-purpose features for assessing the expected difficulty in solving permutation flowshop scheduling problems, hence pushing a step towards a better understanding of the challenges that multi-objective randomized search heuristics have to face.
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Communication dans un congrès
Heike Trautmann; Günter Rudolph; Kathrin Klamroth; Oliver Schütze; Margaret Wiecek; Yaochu Jin; Christian Grimme. 9th International Conference on Evolutionary Multi-Criterion Optimization (EMO 2017), Mar 2017, Münster, Germany. Springer, Lecture Notes in Computer Science, 10173 (422-437), 2017, Evolutionary Multi-Criterion Optimization (EMO 2017). 〈http://www.emo2017.org/〉. 〈10.1007/978-3-319-54157-0_29〉
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Arnaud Liefooghe, Bilel Derbel, Sebastien Verel, Hernan Aguirre, Kiyoshi Tanaka. A fitness landscape analysis of Pareto local search on bi-objective permutation flowshop scheduling problems. Heike Trautmann; Günter Rudolph; Kathrin Klamroth; Oliver Schütze; Margaret Wiecek; Yaochu Jin; Christian Grimme. 9th International Conference on Evolutionary Multi-Criterion Optimization (EMO 2017), Mar 2017, Münster, Germany. Springer, Lecture Notes in Computer Science, 10173 (422-437), 2017, Evolutionary Multi-Criterion Optimization (EMO 2017). 〈http://www.emo2017.org/〉. 〈10.1007/978-3-319-54157-0_29〉. 〈hal-01496357〉

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