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Please use this identifier to cite or link to this item: http://eprint.iitd.ac.in/handle/2074/198

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dc.contributor.authorHarikumar, S-
dc.contributor.authorKumar, Shashi-
dc.date.accessioned2005-05-31T04:28:07Z-
dc.date.available2005-05-31T04:28:07Z-
dc.date.issued1996-
dc.identifier.citationInformation Processing Letters 58 , 11 - 15en
dc.identifier.urihttp://eprint.iitd.ac.in/dspace/handle/2074/198-
dc.description.abstractMany real-world optimization problems involve multiple objectives which are often conflicting. When conventional heuristic search algorithms such as A* and IDA* are used for solving such problems, then these problems have to be modeled as simple cost minimization or maximization problems. The task of modeling such problems using a single valued crite- rion has often proved difficult [6]. The problems involved in accurately and confidently determining a scalar valued criterion on which to base the selection of a most preferred alternative have led to the devel- opment of the multiobjective approach to alternative selection [7]. In [7], Stewart and White have pre- sented a multiobjective generalization of the popular A* algorithm, the MOA* , which uses heuristic based best first search techniques to generate all non- dominated solutions. Like A*, MOA* also has expo- nential space complexity. Depth first search tech- niques use linear space, but take too much time and do not lead to an admissible algorithm.en
dc.format.extent428,482 bytes-
dc.format.mimetypeapplication/pdf-
dc.language.isoen-
dc.publisherElsevier Scienceen
dc.subjectAlgorithmsen
dc.subjectCombinatorial problemsen
dc.subjectMultiobjective searchen
dc.subjectOptimizationen
dc.subjectAnalysis of algorithmsen
dc.titleIterative deepening multiobjectiveen
Appears in Collections:Computer Science and Engineering

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