LEADER 02239nam 2200433 450 001 9910317752303321 005 20240112203144.0 010 $a953-51-5717-5 035 $a(CKB)4970000000098561 035 $a(NjHacI)994970000000098561 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/66429 035 $a(EXLCZ)994970000000098561 100 $a20221017d2013 uy 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aAnt Colony Optimization $eTechniques and Applications /$fHelio J. C. Barbosa, editor 210 $cIntechOpen$d2013 210 1$aRijeka, Croatia :$cIntechOpen,$d[2013] 210 4$dİ2013 215 $a1 online resource (214 pages) $cillustrations 311 $a953-51-1001-2 320 $aIncludes bibliographical references and index. 330 $aAnt Colony Optimization (ACO) is the best example of how studies aimed at understanding and modeling the behavior of ants and other social insects can provide inspiration for the development of computational algorithms for the solution of difficult mathematical problems. Introduced by Marco Dorigo in his PhD thesis (1992) and initially applied to the travelling salesman problem, the ACO field has experienced a tremendous growth, standing today as an important nature-inspired stochastic metaheuristic for hard optimization problems. This book presents state-of-the-art ACO methods and is divided into two parts: (I) Techniques, which includes parallel implementations, and (II) Applications, where recent contributions of ACO to diverse fields, such as traffic congestion and control, structural optimization, manufacturing, and genomics are presented. 517 $aAnt colony optimization 606 $aAnts$xBehavior$xMathematical models 610 $aComputer programming / software development 615 0$aAnts$xBehavior$xMathematical models. 676 $a595.796 700 $aBarbosa$b Helio J.C$4edt$01460884 702 $aBarbosa$b Helio J. C. 801 0$bNjHacI 801 1$bNjHacl 906 $aBOOK 912 $a9910317752303321 996 $aAnt Colony Optimization$93661039 997 $aUNINA