D
Daniel Tuyttens
Researcher at University of Mons
Publications - 67
Citations - 1776
Daniel Tuyttens is an academic researcher from University of Mons. The author has contributed to research in topics: Metaheuristic & Branch and bound. The author has an hindex of 18, co-authored 62 publications receiving 1607 citations. Previous affiliations of Daniel Tuyttens include Faculté polytechnique de Mons & university of lille.
Papers
More filters
Journal ArticleDOI
MOSA method: a tool for solving multiobjective combinatorial optimization problems
TL;DR: In this paper, the authors developed the so-called MOSA (Multiobjective Simulated Annealing) method to approximate the set of efficient solutions of a MOCO problem.
Journal ArticleDOI
A parallel bi-objective hybrid metaheuristic for energy-aware scheduling for cloud computing systems
Mohand Mezmaz,Nouredine Melab,Y. Kessaci,Young Choon Lee,El-Ghazali Talbi,Albert Y. Zomaya,Daniel Tuyttens +6 more
TL;DR: This work proposes a new parallel bi-objective hybrid genetic algorithm that takes into account, not only makespan, but also energy consumption, and focuses on the island parallel model and the multi-start parallel model.
Journal ArticleDOI
Solving multi-objective production scheduling problems using metaheuristics
TL;DR: The aim of this study is to design a general method able to approximate the set of all the efficient schedules for a large set of scheduling models and the method used is called multi-objective simulated annealing.
Journal ArticleDOI
Performance of the MOSA Method for the Bicriteria Assignment Problem
TL;DR: The classical linear Assignment problem is considered with two objectives; an exact method based on the two-phase approach and the so-called MOSA (Multi-Objective Simulated Annealing), which is improved by initialization with a greedy approach.
Journal ArticleDOI
On iterative algorithms for linear least squares problems with bound constraints
TL;DR: Three new iterative methods for the solution of the linear least squares problem with bound constraints are presented and their performance analyzed, with particular emphasis on the dependence on the starting point and the use of preconditioning for ill-conditioned problems.