K
Khaled Ghedira
Researcher at Institut Supérieur de Gestion
Publications - 45
Citations - 1550
Khaled Ghedira is an academic researcher from Institut Supérieur de Gestion. The author has contributed to research in topics: Flow shop scheduling & Job shop scheduling. The author has an hindex of 20, co-authored 45 publications receiving 1284 citations. Previous affiliations of Khaled Ghedira include École Normale Supérieure & Tunis University.
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Journal ArticleDOI
The r-Dominance: A New Dominance Relation for Interactive Evolutionary Multicriteria Decision Making
TL;DR: This paper introduces a new variant of the Pareto dominance relation, called r-dominance, which has the ability to create a strict partial order among Pare to-equivalent solutions and provides competitive and better results when compared to other recently proposed preference-based EMO approaches.
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Discussion and review on evolving data streams and concept drift adapting
TL;DR: This survey covers different facets of existing approaches, evokes discussion and helps readers to underline the sharp criteria that allow them to properly design their own approach to concept drift handling.
Proceedings ArticleDOI
Ant Colony Optimization for Multi-Objective Optimization Problems
TL;DR: A generic algorithm based on ant colony optimization to solve multi-objective optimization problems is proposed and the obtained results are compared with other evolutionary algorithms from the literature.
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A novel chemical reaction optimization for the distributed permutation flowshop scheduling problem with makespan criterion
TL;DR: This paper addresses the Distributed Permutation Flowshop Scheduling Problem (DPFSP) with an artificial chemical reaction metaheuristic which objective is to minimize the maximum completion time and proves the efficiency of the proposed algorithm in comparison with some powerful algorithms.
Journal ArticleDOI
Searching for knee regions of the Pareto front using mobile reference points
TL;DR: An interactive version of TKR-NSGA-II is proposed which is useful when the DM has no a priori information about the number of existing knees in the Pareto optimal front and can provide competitive and better results when compared to other recently proposed methods.