nous présentons les résultats expérimentaux obtenus sur les instances décrites dans la section 3.4.3.1 du chapitre 3 avec le protocole expérimental décrit précédemment. Les figures 4.2 et 4.3 montrent les ensembles d'approximations obtenus avec les algorithmes proposés (IOLD-BMOLS, IOLD-R2-IBMOLS et IOLD-EA) sur 30 exécutions, comparés à NSGA-III, L-NSGA-II et R2-IBMOLS sur trois instances représentatives, une instance avec 2 objectifs et 50 ou 500 actions (figure 4.2) et une autre avec 4 objectifs et 500 actions ,
De plus, par rapport à la direction de préférence (flèche grise) donnée par le point de référence z * =EA sont mieux distribuées dans l'espace bi-objectif de la figure 4.2 et aussi mieux distribuées pour trois sur quatre objectifs de la figure 4.3. Pour la mesure de performance, nous avons choisi la distance euclidienne du point de référence z * appliqué aux ensembles d'approximations obtenus par les différents algorithmes sur un ensemble de 30 instances de différentes tailles. Nous avons calculé les distances minimales, médianes et maximales du point de référence aux ensembles d'approximation obtenus par chaque algorithme. La table 4.1 montre une comparaison entre IOLDNSGA-II et R2-IBMOLS en termes de valeurs moyennes obtenues pour les distances minimales, médianes et maximales sur 30 exécutions (l'approximation ayant la plus petite distance du point de référence est la meilleure) La première colonne contient le nom de l'instance indiqué par sa taille m et n, correspondant respectivement au nombre d'objectifs et au nombre d'actions de l'instance. Chaque cellule de la table 4.1 contient trois valeurs : la distance minimale, la distance médiane et la distance maximale du point de référence (dans cet ordre) Les valeurs écrites dans un style gras signifient que l, D'après les figures 4.2 et 4.3, il est clair que toutes les solutions obtenues les solutions obtenues, pp.555-557 ,
IBMOLS sur 30 exécutions pour les objectifs de quatre instances représentatives, avec 50 ou 500 actions et 5 ou 6 objectifs5_500" (bas gauche) et "5_50, p.71 ,
NSGA-II et R2-IBMOLS pour l'instance "2-50" (gauche) et "2-500, p.92 ,
NSGA-II et R2-IBMOLS pour l'instance "4-500, p.92 ,
Réforme pour une Adéquation des FINancements au Parcours des Personnes Handicapées, p.27 ,
R 2-IBMOLS applied to a practical case of the multiobjective knapsack problem, Liste des publications Revues internationales, pp.457-468, 2017. ,
DOI : 10.1016/j.eswa.2016.11.007
URL : https://hal.archives-ouvertes.fr/hal-01426411
Lorenz dominance based algorithms to solve a practical case of the multiobjective knapsack problem ,
A practical case of the multiobjective knapsack problem : Design, modelling, tests and analysis, no. 8994 in Lecture Notes in Computer Science, pp.249-255 ,
R2-IBMOLS : appliqué à un problème pratique du sac-à-dos multiobjectif, Recherche Opérationnelle et Aide à la DEcision Française (ROADEF'16) proceedings, 2016. ,
Cas pratique pour le problème du sac-à-dos multiobjectif : Conception, modélisation, tests et analyse, Recherche Opérationnelle et Aide à la DEcision Française (ROADEF'15) proceedings, 2015. ,
Agence Nationale d'Appui à la Performance, pp.2013-2026 ,
Anticiper et comprendre In Le secteur médicosocial : comprendre pour agir mieux. Agence Nationale d'Appui à la Performance, pp.2013-50, 2013. ,
Resource management in software as a service using the knapsack problem model, International Journal of Production Economics, vol.141, issue.2, pp.465-477, 2013. ,
DOI : 10.1016/j.ijpe.2011.12.011
MOTGA: A multiobjective Tchebycheff based genetic algorithm for the multidimensional knapsack problem, Computers & Operations Research, vol.34, issue.11, pp.3458-3470, 2007. ,
DOI : 10.1016/j.cor.2006.02.008
Schéma régional d'organisation médico-sociale. Document de travail, ARS Île-de-France, 2016. ,
HypE: An Algorithm for Fast Hypervolume-Based Many-Objective Optimization, Evolutionary Computation, vol.19, issue.1, pp.45-76, 2011. ,
DOI : 10.1109/TEVC.2003.810758
URL : http://www.mitpressjournals.org/userimages/ContentEditor/1164817256746/lib_rec_form.pdf
Genetic tabu search for the multi-objective knapsack problem, Journal of Tsinghua Science and Technology, vol.8, issue.1, pp.8-13, 2003. ,
Indicator-based multi-objective local search, 2007 IEEE Congress on Evolutionary Computation, pp.3100-3107, 2007. ,
DOI : 10.1109/CEC.2007.4424867
URL : https://hal.archives-ouvertes.fr/hal-00609252
Handling Uncertainty in Indicator-Based Multiobjective Optimization, International Journal of Computational Intelligence Research, vol.2, issue.3, pp.255-272, 2006. ,
DOI : 10.5019/j.ijcir.2006.66
The efficiency of indicator-based local search for multi-objective combinatorial optimisation problems, Journal of Heuristics, vol.7, issue.2, pp.263-296, 2012. ,
DOI : 10.1109/TEVC.2003.810758
URL : https://hal.archives-ouvertes.fr/hal-00609252
Hypervolume-based multi-objective local search, Neural Computing and Applications, pp.1917-1929, 2012. ,
DOI : 10.1109/TEVC.2003.810758
Conception d'algorithmes coopératifs pour l'optimisation multi-objectif : application aux problèmes d'ordonnancement de type flow-shop, Thèse, Université Lille 1 - Sciences et technologies, 2005. ,
Brain???Computer Evolutionary Multiobjective Optimization: A Genetic Algorithm Adapting to the Decision Maker, IEEE Transactions on Evolutionary Computation, vol.14, issue.5, pp.671-687, 2010. ,
DOI : 10.1109/TEVC.2010.2058118
Implementing an efficient fptas for the 0???1 multi-objective knapsack problem, European Journal of Operational Research, vol.198, issue.1, pp.47-56, 2009. ,
DOI : 10.1016/j.ejor.2008.07.047
Solving efficiently the 0???1 multi-objective knapsack problem, Computers & Operations Research, vol.36, issue.1, pp.260-279, 2009. ,
DOI : 10.1016/j.cor.2007.09.009
URL : http://l1.lamsade.dauphine.fr/~hugot/articles/Cor07.pdf
Secteur social et médico-social : Regards croisés,enjeux et perspectives, 2013. ,
Searching for knee regions in multiobjective optimization using mobile reference points, Proceedings of the 25th ACM Symposium on Applied Computing, SAC '10, pp.1118-1125, 2010. ,
The r-dominance : A new dominance relation for interactive evolutionary multicriteria decision making, IEEE Transactions on Evolutionary Computation, vol.14, issue.5, pp.801-818, 2010. ,
Searching for knee regions of the Pareto front using mobile reference points, Soft Computing, vol.7, issue.2, pp.1807-1823, 2011. ,
DOI : 10.1109/TEVC.2003.810758
Lamjed Ben Said, and Khaled Ghédira. Preference incorporation in evolutionary multiobjective optimization : A survey of the state-of-the-art ,
A tabu search heuristic for multiobjective knapsack problems, Rutgers Center for Operations Research, 1997. ,
A hybrid heuristic for multiobjective knapsack problems In Meta-heuristics : advances and trends in local search paradigms for optimization, pp.205-212, 1999. ,
Indicator based ant colony optimization for multiobjective knapsack problem, Procedia Computer Science, vol.60, pp.448-457, 2015. ,
A Min-Max Tchebycheff Based Local Search Approach for MOMKP, Proceedings of the 12th International Conference on Software Technologies, pp.140-150, 2017. ,
DOI : 10.5220/0006433801400150
On the Complexity of Computing the Hypervolume Indicator, IEEE Transactions on Evolutionary Computation, vol.13, issue.5, pp.1075-1082, 2009. ,
DOI : 10.1109/TEVC.2009.2015575
Fair Division : From Cake-Cutting to Dispute Resolution, 1996. ,
Integrating user preferences into evolutionary multiobjective optimization, Knowledge Incorporation in Evolutionary Computation, pp.461-477, 2005. ,
DOI : 10.1007/978-3-540-44511-1_21
Improving hypervolume-based multiobjective evolutionary algorithms by using objective reduction methods, 2007 IEEE Congress on Evolutionary Computation, pp.2086-2093, 2007. ,
DOI : 10.1109/CEC.2007.4424730
On the properties of the R2 indicator, Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference, GECCO '12, pp.465-472 ,
DOI : 10.1145/2330163.2330230
URL : https://hal.archives-ouvertes.fr/hal-00722060
2 Indicator-Based Multiobjective Search, Evolutionary Computation, vol.23, issue.3, pp.369-395, 2014. ,
DOI : 10.1109/TEVC.2003.810758
URL : https://hal.archives-ouvertes.fr/hal-01329559
Solving bicriteria 0???1 knapsack problems using a labeling algorithm, Computers & Operations Research, vol.30, issue.12, pp.1865-1886, 2003. ,
DOI : 10.1016/S0305-0548(02)00112-0
Bi-objective branch-and-cut algorithms applied to the binary knapsack problem, Thèse, 2015. ,
URL : https://hal.archives-ouvertes.fr/tel-01242210
A practical case of the multiobjective knapsack problem : Design, modelling, tests and analysis, Learning and Intelligent Optimization, number 8994 in Lecture Notes in Computer Science, pp.249-255, 2015. ,
R 2-IBMOLS applied to a practical case of the multiobjective knapsack problem, Expert Systems with Applications, vol.71, pp.457-468, 2017. ,
DOI : 10.1016/j.eswa.2016.11.007
A ???reduce and solve??? approach for the multiple-choice multidimensional knapsack problem, European Journal of Operational Research, vol.239, issue.2, pp.313-322, 2014. ,
DOI : 10.1016/j.ejor.2014.05.025
Hybrid algorithms for the Multiple-choice Multi-dimensional Knapsack Problem, International Journal of Operational Research, vol.5, issue.1, pp.89-109, 2009. ,
DOI : 10.1504/IJOR.2009.024531
URL : https://hal.archives-ouvertes.fr/hal-00308704
An induction theorem for rearrangements, Canadian Journal of Mathematics, vol.28, issue.1, pp.154-160, 1976. ,
Evolutionary Algorithms for Solving Multi-Objective Problems, 2007. ,
DOI : 10.1007/978-1-4757-5184-0
Preferences and their application in evolutionary multiobjective optimization, IEEE Transactions on Evolutionary Computation, vol.6, issue.1, pp.42-57, 2002. ,
DOI : 10.1109/4235.985691
Pareto simulated annealing?a metaheuristic technique for multiple-objective combinatorial optimization, Journal of Multi-Criteria Decision Analysis, vol.7, issue.1, pp.34-47, 1998. ,
The Measurement of the Inequality of Incomes, The Economic Journal, vol.30, issue.119, pp.348-361, 1920. ,
DOI : 10.2307/2223525
-Based Multi/Many-Objective Particle Swarm Optimization, Computational Intelligence and Neuroscience, vol.25, p.10, 2016. ,
DOI : 10.1007/1-84628-137-7_6
An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part I: Solving Problems With Box Constraints, IEEE Transactions on Evolutionary Computation, vol.18, issue.4, pp.577-601, 2014. ,
DOI : 10.1109/TEVC.2013.2281535
Interactive evolutionary multi-objective optimization and decision-making using reference direction method, Proceedings of the 9th annual conference on Genetic and evolutionary computation , GECCO '07, pp.781-788, 2007. ,
DOI : 10.1145/1276958.1277116
URL : http://www.iitk.ac.in/kangal/papers/k2005003.pdf
A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE Transactions on Evolutionary Computation, vol.6, issue.2, pp.182-197, 2002. ,
DOI : 10.1109/4235.996017
URL : http://work.caltech.edu/amrit/papers/nsga2.ps.gz
Reference point based multi-objective optimization using evolutionary algorithms, In International Journal of Computational Intelligence Research, pp.635-642, 2006. ,
DOI : 10.1145/1143997.1144112
An Interactive Evolutionary Multiobjective Optimization Method Based on Progressively Approximated Value Functions, IEEE Transactions on Evolutionary Computation, vol.14, issue.5, pp.723-739, 2010. ,
DOI : 10.1109/TEVC.2010.2064323
URL : http://www.iitk.ac.in/kangal/papers/k2009005.pdf
Multi-Objective Evolutionary Algorithms, Evolutionary Computation, vol.10, issue.4, pp.173-195, 1999. ,
DOI : 10.1109/TEVC.2005.859463
Using Bound Sets in Multiobjective Optimization: Application to the Biobjective Binary Knapsack Problem, Proceedings of the 9th International Conference on Experimental Algorithms (SEA'10 Italy, 2010. [ Direction des études, statistiques et Prévisions du MASS, pp.2012-2025 ,
DOI : 10.1007/978-3-642-13193-6_22
URL : https://hal.archives-ouvertes.fr/hal-01291385
New multi-objective method to solve reentrant hybrid flow shop scheduling problem, European Journal of Operational Research, vol.203, issue.1, pp.22-31, 2010. ,
DOI : 10.1016/j.ejor.2009.06.031
New multi-objective method to solve reentrant hybrid flow shop scheduling problem, European Journal of Operational Research, vol.203, issue.1, pp.22-31, 2010. ,
DOI : 10.1016/j.ejor.2009.06.031
Fuzzy Lorenz Ant Colony System to solve multiobjective reentrant hybride flowshop scheduling problem, 2011 International Conference on Communications, Computing and Control Applications (CCCA), pp.1-6, 2011. ,
DOI : 10.1109/CCCA.2011.6031495
Bounds and Bound Sets for Biobjective Combinatorial Optimization Problems, Computers & Operations Research, vol.34, issue.9, pp.2674-2694, 2007. ,
DOI : 10.1007/978-3-642-56680-6_22
Multicriteria Optimization, volume 491 of Lecture Notes in Economics and Mathema-tical Systems, 2000. ,
Multicriteria Optimization, p.61, 2005. ,
DOI : 10.1007/978-3-662-22199-0
An EMO Algorithm Using the Hypervolume Measure as Selection Criterion, Evolutionary Multi-Criterion Optimization, number 3410 in Lecture Notes in Computer Science, pp.62-76, 2002. ,
DOI : 10.1007/978-3-540-31880-4_5
Approximating Multiobjective Knapsack Problems, Management Science, vol.48, issue.12, pp.1603-1612, 2002. ,
DOI : 10.1287/mnsc.48.12.1603.445
iMOACO_\mathbb {R} : A new indicator-based multi-objective ant colony optimization algorithm for continuous search spaces, Parallel Problem Solving from Nature ? PPSN XIV, number 9921 in Lecture Notes in Computer Science, pp.389-398, 2016. ,
Algorithmic improvements on dynamic programming for the bi-objective {0,1} knapsack problem, Computational Optimization and Applications, vol.12, issue.2, pp.97-111, 2013. ,
DOI : 10.1023/A:1008258310679
URL : https://hal.archives-ouvertes.fr/hal-01505666
Multiple criteria knapsack problems : network models and computational results, International Conference on Multi-Objective Programming and Goal Programming (MOPGP '06), 2006. ,
Gestion de crise : optimisation de la mise en oeuvre des plans de secours, 4ème Workshop Interdisciplinaire sur la Sécurité Globale, 2010. ,
Genetic algorithms for multiobjective optimization : Formulation, discussion and generalization, Proceedings of the 5th International Conference on Genetic Algorithms (ICGA'93), pp.416-423, 1993. ,
Multiobjective optimization and multiple constraint handling with evolutionary algorithms. I. A unified formulation, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, vol.28, issue.1, pp.26-37, 1998. ,
DOI : 10.1109/3468.650319
URL : http://eprints.whiterose.ac.uk/79978/1/acse%20research%20report%20565.pdf
Exact Methods for Computing All Lorenz Optimal Solutions to Biobjective Problems, Proceedings of the 4th International Conference on Algorithmic Decision Theory, pp.305-321, 2015. ,
DOI : 10.1007/978-3-319-23114-3_19
URL : https://hal.archives-ouvertes.fr/hal-01361196
Multiagent fair optimization with lorenz dominance, Proceedings of the 2015 International Conference on Autonomous Agents and Multiagent Systems, pp.1895-1896, 2015. ,
URL : https://hal.archives-ouvertes.fr/hal-01388539
Evolution du secteur social et médico-social du à la réunion de 1946 à, 2011. ,
Quadratic knapsack problems, Combinatorial Optimization, number 12 in Mathematical Programming Studies, pp.132-149, 1980. ,
DOI : 10.1007/BFb0120892
Tabu search based procedure for solving the ,
URL : https://hal.archives-ouvertes.fr/hal-00462046
Cardinality bounds for multiobjective knapsack problems based on weighted sums scalarizations, Proceedings of the Multi-Objective Programming and Goal Programming Conference (MOPGP'06), 2006. ,
A scatter search method for bi-criteria {0,1}-knapsack problems, European Journal of Operational Research, vol.169, issue.2, pp.373-391, 2006. ,
DOI : 10.1016/j.ejor.2004.08.005
Integrating partial optimization with scatter search for solving bi-criteria {0,1}-knapsack problems, European Journal of Operational Research, vol.177, issue.3, pp.1656-1677, 2007. ,
DOI : 10.1016/j.ejor.2005.10.013
Core problems in bi-criteria -knapsack problems, Computers & Operations Research, vol.35, issue.7, pp.2292-2306, 2008. ,
DOI : 10.1016/j.cor.2006.11.001
Biased random-key genetic algorithms for combinatorial optimization, Journal of Heuristics, vol.17, issue.5, pp.487-525, 1996. ,
Fitness functions for multiple objective optimization problems : Combining preferences with pareto rankings, Proceedings of the 4th Workshop on Foundations of Genetic Algorithms, pp.437-455, 1996. ,
Evaluating the quality of approximations to the non-dominated set, 1998. ,
Métaheuristiques pour l'optimisation combinatoire et l'affectation sous contraintes. Revue d'Intelligence Artificielle, pp.283-324, 1999. ,
Solving the multi-dimensional multichoice knapsack problem with the help of ants, Swarm Intelligence, number 6234 in Lecture Notes in Computer Science, pp.312-323, 2010. ,
Distance-Based Analysis of Crossover Operators for Many-Objective Knapsack Problems, Parallel Problem Solving from Nature ? PPSN XIII, number 8672 in Lecture Notes in Computer Science, p.600 ,
DOI : 10.1007/978-3-319-10762-2_59
Overview of the algorithms for solving the multidimensional knapsack problems, Advanced Studies in Biology, vol.4, issue.1, pp.37-47, 2012. ,
On the performance of multiple-objective genetic local search on the 0/1 knapsack problem - a comparative experiment, IEEE Transactions on Evolutionary Computation, vol.6, issue.4, pp.402-412, 2002. ,
DOI : 10.1109/TEVC.2002.802873
A Simple and Fast Hypervolume Indicator-Based Multiobjective Evolutionary Algorithm, IEEE Transactions on Cybernetics, vol.45, issue.10, pp.452202-2213, 2015. ,
DOI : 10.1109/TCYB.2014.2367526
Fuzzy preference incorporation into evolutionary multiobjective optimization, Proceedings of the 4th Asia-Pacific Conference on Simulated Evolution and Learning, pp.26-30, 2002. ,
Nouvelles propositions pour la résolution exacte du sac à dos multi-objectif unidimensionnel en variables binaires, Thèse, pp.47-47, 2004. ,
Knapsack Problems, 2004. ,
DOI : 10.1007/978-3-540-24777-7
Dynamic programming approaches to the multiple criteria knapsack problem, Naval Research Logistics (NRL), vol.38, issue.1, pp.57-76, 2000. ,
DOI : 10.1007/BF01584241
A tutorial on the performance assessment of stochastic multiobjective optimizers, 2006. ,
An Interactive Territory Defining Evolutionary Algorithm: iTDEA, IEEE Transactions on Evolutionary Computation, vol.14, issue.5, pp.702-722, 2010. ,
DOI : 10.1109/TEVC.2010.2070070
A visual interactive method for solving the multiple criteria problem, European Journal of Operational Research, vol.24, issue.2, pp.277-287, 1986. ,
Equitable Approaches to Location Problems, 1999. ,
Linear optimization with multiple equitable criteria, RAIRO-Operations Research, vol.33, issue.3, pp.275-297, 1999. ,
Equitable aggregations and multiple criteria analysis, European Journal of Operational Research, vol.158, issue.2, pp.362-377, 2004. ,
Analysis of a Multiobjective Evolutionary Algorithm on the 0???1 knapsack problem, Theoretical Computer Science, vol.358, issue.1, pp.104-120, 2006. ,
DOI : 10.1016/j.tcs.2006.03.007
R2-MOPSO: A multi-objective particle swarm optimizer based on R2-indicator and decomposition, 2015 IEEE Congress on Evolutionary Computation (CEC), pp.3148-3155, 2008. ,
DOI : 10.1109/CEC.2015.7257282
Metaheuristics and their hybridization to solve the bi-objective ring star problem : a comparative study, 2008. ,
URL : https://hal.archives-ouvertes.fr/inria-00275659
Métaheuristiques pour l'optimisation multiobjectif : approches coopératives , prise en compte de l'incertitude et application en logistique, Thèse, Université Lille 1 -Sciences et technologies, 2009. ,
Many-objective evolutionary optimization based on reference points, Applied Soft Computing, vol.50, pp.344-355, 2017. ,
DOI : 10.1016/j.asoc.2016.11.009
Two-phase Pareto local search for the biobjective traveling salesman problem, Journal of Heuristics, vol.7, issue.2, pp.475-510, 2010. ,
DOI : 10.1287/ijoc.3.4.376
The multiobjective multidimensional knapsack problem: a survey and a new approach, International Transactions in Operational Research, vol.7, issue.2, pp.495-520, 2012. ,
DOI : 10.1109/TEVC.2003.810758
De la gouvernance des établissements à la gouvernance des territoires, 2008. ,
Knapsack Problems : Algorithms and Computer Implementations, 1990. ,
The labeling algorithm for the multiobjective shortest path problem, 1999. ,
Nonlinear Multiobjective Optimization, 1999. ,
DOI : 10.1007/978-1-4615-5563-6
Des activités et des métiers du secteur social, médico-social et petite enfance, 2010. ,
Lorenz versus Pareto Dominance in a Single Machine Scheduling Problem with Rejection, Proceedings of the 6th International Conference on Evolutionary Multi-criterion Optimization, pp.520-534, 2011. ,
DOI : 10.1109/TEVC.2003.810758
A multi-criteria approach to fair and efficient bandwidth allocation???, Omega, vol.36, issue.3, pp.451-463, 2008. ,
DOI : 10.1016/j.omega.2005.12.005
Fair Optimization and Networks: A Survey, Journal of Applied Mathematics, vol.2002, issue.3, pp.1-26, 2014. ,
DOI : 10.1155/2013/480962
URL : https://hal.archives-ouvertes.fr/hal-01120937
Inequality measures and equitable approaches to location problems, European Journal of Operational Research, vol.122, issue.2, pp.374-391, 2000. ,
DOI : 10.1016/S0377-2217(99)00240-4
Multiple criteria linear programming model for portfolio selection, Annals of Operations Research, vol.97, issue.1/4, pp.143-162, 2000. ,
DOI : 10.1023/A:1018980308807
Conception des outils pour le suivi des activités et l'aide au pilotage dans le secteur médico-social, Thèse, Institut national des sciences appliquées de Lyon, 2015. ,
Combinatorial optimization : algorithms and complexity, 1982. ,
Pareto Local Optimum Sets in the Biobjective Traveling Salesman Problem: An Experimental Study, Metaheuristics for Multiobjective Optimisation, number 535 in Lecture Notes in Economics and Mathematical Systems, pp.177-199, 2004. ,
DOI : 10.1007/978-3-642-17144-4_7
Infinite order lorenz dominance for fair multiagent optimization, Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems, pp.383-390, 2010. ,
URL : https://hal.archives-ouvertes.fr/hal-01291403
A decision-theoretic approach to robust optimization in multivalued graphs, Annals of Operations Research, vol.25, issue.6, pp.317-341, 2006. ,
DOI : 10.1145/115234.115368
URL : https://hal.archives-ouvertes.fr/hal-01170404
R2-IBEA: R2 indicator based evolutionary algorithm for multiobjective optimization, 2013 IEEE Congress on Evolutionary Computation, pp.1836-1845 ,
DOI : 10.1109/CEC.2013.6557783
R2-BEAN: R2 indicator based evolutionary algorithm for noisy multiobjective optimization, the 2014 Seventh IEEE Symposium on Computational Intelligence for Security and Defense Applications (CISDA), pp.1-8, 2014. ,
DOI : 10.1109/CISDA.2014.7035637
The quadratic knapsack problem???a survey, Discrete Applied Mathematics, vol.155, issue.5, pp.623-648, 2007. ,
DOI : 10.1016/j.dam.2006.08.007
MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition, IEEE Transactions on Evolutionary Computation, vol.11, issue.6, pp.712-731, 2007. ,
DOI : 10.1109/TEVC.2007.892759
Preference Incorporation in Multi-objective Evolutionary Algorithms: A Survey, 2006 IEEE International Conference on Evolutionary Computation, pp.962-968, 2006. ,
DOI : 10.1109/CEC.2006.1688414
Incorporating the Notion of Relative Importance of Objectives in Evolutionary Multiobjective Optimization, IEEE Transactions on Evolutionary Computation, vol.14, issue.4, pp.530-546, 2010. ,
DOI : 10.1109/TEVC.2009.2036162
The knapsack problem : A survey, Naval Research Logistics Quarterly, vol.22, issue.79, pp.127-144, 1973. ,
On Economic Inequality, 1973. ,
DOI : 10.1093/0198281935.001.0001
A fast and scalable multidimensional multiple-choice knapsack heuristic, ACM Transactions on Design Automation of Electronic Systems, vol.18, issue.4, pp.1-5132, 2013. ,
DOI : 10.1145/2541012.2541014
The Multiple-Choice Knapsack Problem, Operations Research, vol.27, issue.3, pp.503-515, 1979. ,
DOI : 10.1287/opre.27.3.503
Sur la division pragmatique, Econometrica, vol.17, pp.315-319, 1949. ,
DOI : 10.2307/1907319
Metaheuristics : From Design to Implementation, p.60, 1999. ,
Evolutionary algorithms with goal and priority information for multi-objective optimization, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406), pp.106-113, 1999. ,
DOI : 10.1109/CEC.1999.781914
An evolutionary algorithm with advanced goal and priority specification for multi-objective optimization, Journal of Artificial Intelligence Research, vol.18, pp.183-215, 2003. ,
A multi-objective local search heuristic for scheduling Earth observations taken by an agile satellite, European Journal of Operational Research, vol.245, issue.2, pp.542-554, 2015. ,
DOI : 10.1016/j.ejor.2015.03.011
URL : https://hal.archives-ouvertes.fr/hal-01162839
Genetic algorithm and local search comparison for solving biobjective p-median problem, Proceedings of the 2015 International Conference on Informatics , Electronics Vision (ICIEV), pp.1-5, 2015. ,
A Preference-Based Evolutionary Algorithm for Multi-Objective Optimization, Evolutionary Computation, vol.17, issue.3, pp.411-436, 2009. ,
DOI : 10.1162/106365600568202
R2-EMOA: Focused Multiobjective Search Using R2-Indicator-Based Selection, 7th International Learning and Intelligent Optimization Conference, pp.70-74, 2013. ,
DOI : 10.1007/978-3-642-44973-4_8
URL : https://hal.archives-ouvertes.fr/hal-00807901
The two phase method : An efficient procedure to solve bi-objective combinatorial optimization problems, Foundations of Computing and Decision Sciences, vol.20, issue.2, pp.149-165, 1995. ,
Multi-objective combinatorial optimization problems: A survey, Journal of Multi-Criteria Decision Analysis, vol.37, issue.2, pp.83-104, 1994. ,
DOI : 10.1108/eb014658
Solving Multi-Objective Knapsack Problem by a Branch-and-Bound Procedure, Multicriteria Analysis BIBLIOGRAPHIE [ Ulungu and Teghem, pp.269-278, 1997. ,
DOI : 10.1007/978-3-642-60667-0_26
MOSA method : A tool for solving multiobjective combinatorial optimization problems, Journal of Multi-Criteria Decision Analysis, vol.8, issue.4, pp.221-236, 1999. ,
Local searchbased heuristics for the multiobjective multidimensional knapsack problem, Production, vol.23, issue.3, pp.478-487, 2013. ,
A memory-based GRASP heuristic for the multiobjective multidimensional knapsack problem, International Journal of Latest Research in Science and Technology, vol.3, issue.4, pp.186-194, 2014. ,
Two-phases method and branch and bound procedures to solve the bi?objective knapsack problem, Journal of Global Optimization, vol.12, issue.2, pp.139-155, 1998. ,
DOI : 10.1023/A:1008258310679
Preference Articulation by Means of the R2 Indicator, Evolutionary Multi-Criterion Optimization, number 7811 in Lecture Notes in Computer Science, pp.81-95, 2013. ,
DOI : 10.1007/978-3-642-37140-0_10
URL : https://hal.archives-ouvertes.fr/hal-00807867
Sequential parameter optimization for multi-objective problems, IEEE Congress on Evolutionary Computation, pp.1-8, 2010. ,
DOI : 10.1109/CEC.2010.5586529
A survey of effective heuristics and their application to a variety of knapsack problems, IMA Journal of Management Mathematics, vol.19, issue.3, pp.227-244, 2008. ,
DOI : 10.1093/imaman/dpn004
Solving bi-objective flow shop problem with hybrid path relinking algorithm, Applied Soft Computing, vol.13, issue.10, pp.4118-4132, 2013. ,
DOI : 10.1016/j.asoc.2013.05.018
Indicator-Based Selection in Multiobjective Search, 8th International Conference on Parallel Problem Solving from Nature (PPSN VIII), pp.832-842, 2004. ,
DOI : 10.1007/978-3-540-30217-9_84
Performance assessment of multiobjective optimizers: an analysis and review, IEEE Transactions on Evolutionary Computation, vol.7, issue.2, pp.117-132, 2003. ,
DOI : 10.1109/TEVC.2003.810758
The Hypervolume Indicator Revisited: On the Design of Pareto-compliant Indicators Via Weighted Integration, Evolutionary Multi-Criterion Optimization, number 4403 in Lecture Notes in Computer Science, pp.862-876, 2007. ,
DOI : 10.1007/978-3-540-70928-2_64
SPEA2 : Improving the strength pareto evolutionary algorithm for multiobjective optimization, Evolutionary Methods for Design, Optimisation , and Control with Application to Indusrial Problems, pp.95-100, 2012. ,
A cooperative swarm intelligence algorithm for multi-objective discrete optimization with application to the knapsack problem, European Journal of Operational Research, vol.264, issue.1, 2017. ,
DOI : 10.1016/j.ejor.2017.06.058