LEADER 02948nam  2200505Ia 450 
001 9910299735003321
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010   $a3-642-40179-1
024 7 $a10.1007/978-3-642-40179-4
035   $a(OCoLC)859156083
035   $a(MiFhGG)GVRL6XFG
035   $a(CKB)2670000000423165
035   $a(MiAaPQ)EBC1398838
035   $a(EXLCZ)992670000000423165
100   $a20111102d2014      uy         0
101 0 $aeng
135   $aurun|---uuuua
181   $ctxt
182   $cc
183   $acr
200 10$aAdvances in bio-inspired computing for combinatorial optimization problems /$fCamelia-Mihaela Pintea
205   $a1st ed. 2014.
210   $aBerlin ;$aHeidleberg $cSpringer-Verlag$d2014
215   $a1 online resource (x, 188 pages) $cillustrations (some color)
225 0 $aIntelligent systems reference library ;$v57
300   $a"ISSN: 1868-4394."
311   $a3-642-43877-6 
311   $a3-642-40178-3 
320   $aIncludes bibliographical references and index.
327   $aPart I Biological Computing and Optimization -- Part II Ant Algorithms -- Part III Bio-inspired Multi-Agent Systems -- Part IV Applications with Bio-inspired Algorithms -- Part V Conclusions and Remarks.
330   $a"Advances in Bio-inspired Combinatorial Optimization Problems" illustrates several recent bio-inspired efficient algorithms for solving NP-hard problems. Theoretical bio-inspired concepts and models, in particular for agents, ants and virtual robots are described. Large-scale optimization problems, for example: the Generalized Traveling Salesman Problem and the Railway Traveling Salesman Problem, are solved and their results are discussed. Some of the main concepts and models described in this book are: inner rule to guide ant search - a recent model in ant optimization, heterogeneous sensitive ants; virtual sensitive robots; ant-based techniques for static and dynamic routing problems; stigmergic collaborative agents and learning sensitive agents. This monograph is useful for researchers, students and all people interested in the recent natural computing frameworks. The reader is presumed to have knowledge of combinatorial optimization, graph theory, algorithms and programming. The book should furthermore allow readers to acquire ideas, concepts and models to use and develop new software for solving complex real-life problems.
410  0$aIntelligent systems reference library ;$vvolume 57.
606   $aBiologically-inspired computing
606   $aCombinatorial optimization
615  0$aBiologically-inspired computing.
615  0$aCombinatorial optimization.
676   $a006.3
700   $aPintea$b Camelia-Mihaela$0956514
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a9910299735003321
996   $aAdvances in Bio-inspired Computing for Combinatorial Optimization Problems$92165821
997   $aUNINA