Combining Metaheuristics and Exact Methods for Solving Exactly Multi-objective Problems on the Grid

Mezmaz, Mohand-Said; Melab, Nouredine; Talbi, El‐Ghazali

doi:10.1007/s10852-007-9063-8

Cited by 8 publications

(3 citation statements)

References 6 publications

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“…Moreover, a Pareto-based approach could also support the identification of preferences by providing the decision-maker with a set of feasible, objectively equally-valued, highquality solutions as an excellent starting point for selecting the (subjectively) best solution. In the literature on Pareto-based approaches, there are few reports on exact algorithms for solving multi-objective problems (e.g., Dhaenens, Lemesre, and Talbi 2010, Bérubé, Gendreau, and Potvin 2009, Mezmaz, Melab, and Talbi 2007. However, the applicability of exact algorithms still is often limited to rather small instances.…”

Section: Multi-objective Optimization Approachesmentioning

confidence: 99%

GRASP with Hybrid Path Relinking for Bi-Objective Winner Determination in Combinatorial Transportation Auctions

Buer

Pankratz

2010

Bus Res

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in

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Section: Multi-objective Optimization Approachesmentioning

confidence: 99%

GRASP with Hybrid Path Relinking for Bi-Objective Winner Determination in Combinatorial Transportation Auctions

Buer

Pankratz

2010

Bus Res

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“…The combination of the constituent from various metaheuristics is presently one of the most fruitful tendencies in optimization field. In fact, considerable algorithms that do not preserve the design of single classical metaheuristic have been developed ( [21], [22], [23]). The principal incentive for the hybridization of those algorithms is to combine the qualities and the advantages of the individual combined approaches, minimize the effect of their corresponding disadvantages and subsequently enhance the systems performance.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Metaheuristics for Solving the Blocking Flowshop Scheduling Problem

Lebbar

Barkany

Jabri

et al. 2018

JERA

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This paper suggests two evolutionary optimization approaches for solving the blocking flow shop scheduling problem with the maximum completion time (makespan) criterion, namely the genetic algorithm (GA) and the simulated annealing genetic algorithms (SAGA) that combines the simulated annealing (SA) with the (GA), respectively. The considered problem and the proposed algorithms have some parameters to be adjusted through a design of experiments with exorbitant runs. In fact, a Taguchi method is presented to study the parameterization problem empirically. The performance of the proposed algorithms is evaluated by applying it to Taillard’s well-known benchmark problem, the experiment results show that the SA combined with GA method is advanced to the GA and to the compared algorithms proposed in the literature in minimizing makespan criterion. Ultimately, new known upper bounds for Taillard’s instances are reported for this problem, which can be used thereafter as a basis of benchmark in eventual investigations.

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“…As illustrated in Fig. 2.1, the hybrids can also be deployed in the form of multi-populations or multi-islands or ensembles [193,123,161,233]. These independent hybrid models cooperate through the exchange of genetic or memetic information or otherwise.…”

Section: Level Of Hybridizationmentioning

confidence: 99%

A study on memetic computation, with applications to capacitated vehicle routing problems

Chen¹

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Memetic computation, a paradigm that uses the notion of meme(s) as units of information encoded in computational representations for the purpose of problem-solving, represents one of the frontiers in the computational intelligence research, of today's optimization research. This dissertation takes an explorative attitude in the design of memetic computing frameworks and algorithms by leveraging the co-adaptive nature of meme complexes or memeplexes. The current research presented in this dissertation takes multi-memes and memeplexes as focal point of interest in the context of memetic computation and develop meme-centric computing frameworks for more effective problem-solving in the context of stochastic optimization of Capacitated Vehicle Routing Problems (CVRP) and Vehicle Routing Problem with Stochastic Demands (VRPSD). His sincere encouragement during my indecisive moments and inspirational insights into the many aspects of my research direction have faithfully guided me throughout my research work. Without Associate Professor Ong's guidance, the fruitful achievements of this project would not have been possible. I would also like to thank Associate Professor Lim Meng Hiot for his beneficial comments and support. In addition, I would like to thank my friends for their encouragements and the staffs in Intelligent Systems Centre (IntelliSys) and Center for Computational Intelligence for their technical supports. Last but not least, special thanks to my parents for their constant support, understanding and love.

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Combining Metaheuristics and Exact Methods for Solving Exactly Multi-objective Problems on the Grid

Cited by 8 publications

References 6 publications

GRASP with Hybrid Path Relinking for Bi-Objective Winner Determination in Combinatorial Transportation Auctions

GRASP with Hybrid Path Relinking for Bi-Objective Winner Determination in Combinatorial Transportation Auctions

Hybrid Metaheuristics for Solving the Blocking Flowshop Scheduling Problem

A study on memetic computation, with applications to capacitated vehicle routing problems

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