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SOIE PresentationSOIE PresentationStratégies d’Optimisation et Informatique intelligentE-Research thematic : knowledge En...
Outline2. Problematic3. State of the art4. Proposed approach5. Obtained results6. Conclusion and perspectives1. Introducti...
4Transport problems can have effects on the environment atdifferent levels: Global; regional ; local.[ONU 2001]Introduc...
5The improved transport system seems to be a necessitybecause its complexity is a reality. This system is alsoaffected by ...
S4S7S2S3S1S8S5S6: PassengersS : Station: VehicleParametres (departure T,arrival T...): DestinationOptimise the tours of ve...
7DARP resolution(Psaraftis, 1980) (Cordeau&laporte,2003) (Stefan, 2005) (Mauri et al,2006) (Claudio et al,2009) (Zidi et a...
8PresentationOrigin : Darwins theory of evolution Coding chromosomal structures natural selection Evolution operatorsS...
9No-elitistType of MOEA : Multi-Objective Evolutionary AlgorithmsElitistState of the art(GA)
10Genetic Algorithm NSGA2PresentationNSGAII (Elitist Non-dominated Sorting Genetic Algorithm) Proposed by Deb and his tea...
11Destributed Genetic Algorithms (DGA)[S.Bouamama,2008]Multi-agent systemSpecies distribution(Max-CSP)Proposed approach
 Interface agent:- Generate randomly the initial population.- Create species agents for each sub-population.- Create new ...
Distribution of NSGA213Interface Agentinitial population EvaluationRank1 Rank2 Rank3 … Rank nNon dominated sorting…Sélecti...
14The distributed genetic algorithm NSGA2Creation of initial population (cities, deposits, connection ...)Sort by rankDoCr...
Local genetic algorithm for species agents1- Crossover of the selected sub-population.2- Update the obtained sub-populatio...
16duration of the road according to the number of requestsInstance1 (24requests)Instance2 (36requests)Instance3(48requests...
17Execution time based on the number of requestsInstance1(24requests)Instance2(36requests)Instance3(48 requests)Instance4(...
18Duration of the road according to the number ofrequestsExecution time based on the number of requestsEfficiency and impr...
19 Modeling of DARP with two objectives Resolution of DARP for Distributed genetic algorithm NSGA-II Use of multi-agent...
20 Application of the approach on real data. Hybridization of DGA NSGA II with other accurate methods andalgorithms.Pers...
21Thank you for your attentionalayaraddaoui@gmail.com
Alaya 30  10
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  • Ona basé sur le travail de Mr sadok bouamama 2008,
  • Transcript of "Alaya 30 10"

    1. 1. Distributed Genetic Algorithm NSGA IIfor solving the DARPAlaya raddaoui1and Kamel zidi21 alayaraddaoui@gmail.com2 kamel_zidi@yahoo.fr
    2. 2. SOIE PresentationSOIE PresentationStratégies d’Optimisation et Informatique intelligentE-Research thematic : knowledge Engineering and reasoning: Reasoning and Optimization under Constraints Multi-Agent Systems Systems, information and services engineering: Systems and Information Engineering Services Engineering-Adress:Laboratoire de recherche Stratégies d’Optimisation et Informatique intlligentE SOIEISG Tunis, 41, Rue de la Liberté, Cité Bouchoucha 2000 Le bardo, Tunis-TUNISIE 2
    3. 3. Outline2. Problematic3. State of the art4. Proposed approach5. Obtained results6. Conclusion and perspectives1. Introduction3
    4. 4. 4Transport problems can have effects on the environment atdifferent levels: Global; regional ; local.[ONU 2001]Introduction
    5. 5. 5The improved transport system seems to be a necessitybecause its complexity is a reality. This system is alsoaffected by the following phenomena: Social; Economic; Structural.Introduction
    6. 6. S4S7S2S3S1S8S5S6: PassengersS : Station: VehicleParametres (departure T,arrival T...): DestinationOptimise the tours of vehicles to answerthe passengers requests 6ProblematicDial a Ride Problem: DARP
    7. 7. 7DARP resolution(Psaraftis, 1980) (Cordeau&laporte,2003) (Stefan, 2005) (Mauri et al,2006) (Claudio et al,2009) (Zidi et al,10)Exact algorithm:dynamicprogrammingTaboo searchalgorithmBranch andBoundmethodSimulatedannealingalgorithmGeneticalgorithmMultiobjectivesimulatedannealingalgorithmState of the art(DARP)
    8. 8. 8PresentationOrigin : Darwins theory of evolution Coding chromosomal structures natural selection Evolution operatorsSelectionCrossingMutation [Goldberg 89]State of the art(GA)
    9. 9. 9No-elitistType of MOEA : Multi-Objective Evolutionary AlgorithmsElitistState of the art(GA)
    10. 10. 10Genetic Algorithm NSGA2PresentationNSGAII (Elitist Non-dominated Sorting Genetic Algorithm) Proposed by Deb and his team[2000] Based on three characteristics: The principle of elitism The non-dominated solutions Variety of explicit solutions[Deb and 2000]State of the art(GA)
    11. 11. 11Destributed Genetic Algorithms (DGA)[S.Bouamama,2008]Multi-agent systemSpecies distribution(Max-CSP)Proposed approach
    12. 12.  Interface agent:- Generate randomly the initial population.- Create species agents for each sub-population.- Create new agents species if they exist.- Detect the best partial solution. Specie agent:- Execute his own distributed genetic algorithm.Proposed approachOur multi-agents architecture12
    13. 13. Distribution of NSGA213Interface Agentinitial population EvaluationRank1 Rank2 Rank3 … Rank nNon dominated sorting…Sélection agent Crossing agent Mutation agentSélection agent Crossing agent Mutation agentSélection agent Crossing agent Mutation agentSpecies1 agentSpecies3 agentSpecies2 agentProposed approach
    14. 14. 14The distributed genetic algorithm NSGA2Creation of initial population (cities, deposits, connection ...)Sort by rankDoCreating an agent for each species rankLaunch the local genetic algorithm to each agent speciesExchange of individuals crossingExchange of new individualsWihle (Number of generations reached)Proposed approach
    15. 15. Local genetic algorithm for species agents1- Crossover of the selected sub-population.2- Update the obtained sub-population (Child).3- Mutation of the sub-population child crossed.4- Update the mutated sub-population child.Proposed approach
    16. 16. 16duration of the road according to the number of requestsInstance1 (24requests)Instance2 (36requests)Instance3(48requests)Instance4 (72requests)Instance5(120requests)AGD(NSGAII)1249,156 vehiculs2150,468 vehiculs4003.958 vehiculsRSMO (Zidiet all 10) 1414,383 vehiculs1407,68 vehiculs1808,9911 vehiculs2270,864O20 ,7513 vehiculs1436,233 vehiculs1404,44 vehiculsObtained Results
    17. 17. 17Execution time based on the number of requestsInstance1(24requests)Instance2(36requests)Instance3(48 requests)Instance4(72requests)Instance4(120requests)DGA(NSGAII) 0 ,71 2,88 3 ,08 3,86 6,77RSMO (Zidiet all 10)0,57 2,32 4,70 4,90 9,61Obtained Results
    18. 18. 18Duration of the road according to the number ofrequestsExecution time based on the number of requestsEfficiency and improving 4/5 times (duration of theroad) and 3/5 times (run of time)Obtained Results
    19. 19. 19 Modeling of DARP with two objectives Resolution of DARP for Distributed genetic algorithm NSGA-II Use of multi-agent system approach to distribution of the algorithmNSGA IIConclusionConclusion and perspectives
    20. 20. 20 Application of the approach on real data. Hybridization of DGA NSGA II with other accurate methods andalgorithms.PerspectivesConclusion and perspectives
    21. 21. 21Thank you for your attentionalayaraddaoui@gmail.com

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