Improved WTA problem solving method using a parallel genetic algorithm which applied the RMI initialization method
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Hong, S.-S. | - |
dc.contributor.author | Yun, J. | - |
dc.contributor.author | Choi, B. | - |
dc.contributor.author | Kong, J. | - |
dc.contributor.author | Han, M.-M. | - |
dc.date.available | 2020-02-29T09:45:46Z | - |
dc.date.created | 2020-02-11 | - |
dc.date.issued | 2012 | - |
dc.identifier.issn | 0000-0000 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/17514 | - |
dc.description.abstract | The problem of Weapon Target Allocation (WTA) is to find an optimum solution, the type of vector that our weapons assign to targets, to minimize the damage of our assets from the target of an enemy offending us. we proposed the novel parallel genetic algorithm for solved to the WTA problem. The proposed. As the first step, our proposed algorithm is to expand the problem search space through the Random Mutation Inherit (RMI) population initialization method thereby improving convergence performance. We proposed an algorithm which obtains the WTA solution quickly and solves the WTA problem efficiently. © 2012 IEEE. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.relation.isPartOf | 6th International Conference on Soft Computing and Intelligent Systems, and 13th International Symposium on Advanced Intelligence Systems, SCIS/ISIS 2012 | - |
dc.subject | Convergence performance | - |
dc.subject | Intialization | - |
dc.subject | Parallel genetic algorithms | - |
dc.subject | Parallel process | - |
dc.subject | Population initializations | - |
dc.subject | Problem Solving methods | - |
dc.subject | Weapon assignment | - |
dc.subject | Weapon-target allocation | - |
dc.subject | Genetic algorithms | - |
dc.subject | Optimization | - |
dc.subject | Soft computing | - |
dc.subject | Intelligent systems | - |
dc.title | Improved WTA problem solving method using a parallel genetic algorithm which applied the RMI initialization method | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.description.journalClass | 1 | - |
dc.identifier.doi | 10.1109/SCIS-ISIS.2012.6505315 | - |
dc.identifier.bibliographicCitation | 6th International Conference on Soft Computing and Intelligent Systems, and 13th International Symposium on Advanced Intelligence Systems, SCIS/ISIS 2012, pp.2189 - 2193 | - |
dc.identifier.scopusid | 2-s2.0-84877796701 | - |
dc.citation.endPage | 2193 | - |
dc.citation.startPage | 2189 | - |
dc.citation.title | 6th International Conference on Soft Computing and Intelligent Systems, and 13th International Symposium on Advanced Intelligence Systems, SCIS/ISIS 2012 | - |
dc.contributor.affiliatedAuthor | Hong, S.-S. | - |
dc.contributor.affiliatedAuthor | Yun, J. | - |
dc.contributor.affiliatedAuthor | Choi, B. | - |
dc.contributor.affiliatedAuthor | Kong, J. | - |
dc.contributor.affiliatedAuthor | Han, M.-M. | - |
dc.type.docType | Conference Paper | - |
dc.subject.keywordAuthor | Genetic Algorithm | - |
dc.subject.keywordAuthor | Optimization | - |
dc.subject.keywordAuthor | Parallel Process | - |
dc.subject.keywordAuthor | Population Intialization | - |
dc.subject.keywordAuthor | Weapon Assignment | - |
dc.subject.keywordPlus | Convergence performance | - |
dc.subject.keywordPlus | Intialization | - |
dc.subject.keywordPlus | Parallel genetic algorithms | - |
dc.subject.keywordPlus | Parallel process | - |
dc.subject.keywordPlus | Population initializations | - |
dc.subject.keywordPlus | Problem Solving methods | - |
dc.subject.keywordPlus | Weapon assignment | - |
dc.subject.keywordPlus | Weapon-target allocation | - |
dc.subject.keywordPlus | Genetic algorithms | - |
dc.subject.keywordPlus | Optimization | - |
dc.subject.keywordPlus | Soft computing | - |
dc.subject.keywordPlus | Intelligent systems | - |
dc.description.journalRegisteredClass | scopus | - |
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