02443nam 2200553z- 450 991063778010332120231214133150.03-0365-5570-6(CKB)5470000001631736(oapen)https://directory.doabooks.org/handle/20.500.12854/94530(EXLCZ)99547000000163173620202212d2022 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierApplied Metaheuristic ComputingBaselMDPI - Multidisciplinary Digital Publishing Institute20221 electronic resource (684 p.)3-0365-5569-2 For 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.Technology: general issuesbicsscHistory of engineering & technologybicsscmetaheuristicsheuristicsoptimizationartificial intelligenceenergyinformation securityrecognitionTechnology: general issuesHistory of engineering & technologyYin Peng-Yengedt1293396Chang Ray-IedtGheraibia YoucefedtChuang Ming-ChinedtLin Hua-YiedtLee Jen-ChunedtYin Peng-YengothChang Ray-IothGheraibia YoucefothChuang Ming-ChinothLin Hua-YiothLee Jen-ChunothBOOK9910637780103321Applied Metaheuristic Computing3022568UNINA