LEADER 02443nam 2200553z- 450 001 9910637780103321 005 20231214133150.0 010 $a3-0365-5570-6 035 $a(CKB)5470000001631736 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/94530 035 $a(EXLCZ)995470000001631736 100 $a20202212d2022 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aApplied Metaheuristic Computing 210 $aBasel$cMDPI - Multidisciplinary Digital Publishing Institute$d2022 215 $a1 electronic resource (684 p.) 311 $a3-0365-5569-2 330 $aFor decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC. 606 $aTechnology: general issues$2bicssc 606 $aHistory of engineering & technology$2bicssc 610 $ametaheuristics 610 $aheuristics 610 $aoptimization 610 $aartificial intelligence 610 $aenergy 610 $ainformation security 610 $arecognition 615 7$aTechnology: general issues 615 7$aHistory of engineering & technology 700 $aYin$b Peng-Yeng$4edt$01293396 702 $aChang$b Ray-I$4edt 702 $aGheraibia$b Youcef$4edt 702 $aChuang$b Ming-Chin$4edt 702 $aLin$b Hua-Yi$4edt 702 $aLee$b Jen-Chun$4edt 702 $aYin$b Peng-Yeng$4oth 702 $aChang$b Ray-I$4oth 702 $aGheraibia$b Youcef$4oth 702 $aChuang$b Ming-Chin$4oth 702 $aLin$b Hua-Yi$4oth 702 $aLee$b Jen-Chun$4oth 906 $aBOOK 912 $a9910637780103321 996 $aApplied Metaheuristic Computing$93022568 997 $aUNINA