LEADER 04534nam 22005655 450 001 9910765496303321 005 20231116190737.0 010 $a3-031-38310-9 024 7 $a10.1007/978-3-031-38310-6 035 $a(MiAaPQ)EBC30951805 035 $a(Au-PeEL)EBL30951805 035 $a(DE-He213)978-3-031-38310-6 035 $a(EXLCZ)9928863490100041 100 $a20231116d2023 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aDiscrete Diversity and Dispersion Maximization$b[electronic resource] $eA Tutorial on Metaheuristic Optimization /$fedited by Rafael Martí, Anna Martínez-Gavara 205 $a1st ed. 2023. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2023. 215 $a1 online resource (350 pages) 225 1 $aSpringer Optimization and Its Applications,$x1931-6836 ;$v204 311 08$aPrint version: Martí, Rafael Discrete Diversity and Dispersion Maximization Cham : Springer International Publishing AG,c2023 9783031383090 327 $aPart I Models: Discrete diversity optimization. Models and instances (Mart?nez-Gavara) -- The Origins of Discrete Diversity (Kuby) -- Geometrical Analysis of Solutions (Alcaraz) -- Part II Constructive based Metaheuristics: Constructive and destructive methods in heuristic search (Aringhieri) -- Greedy Randomized Adaptive Search Procedure (Sánchez-Oro) -- Iterated Greedy (Lozano) -- Part III Trajectory based Metaheuristics: Tabu Search (Martínez-Gavara) -- Variable neighborhood search (Uro?evi?) -- Less is More approach (Todosijevi?) -- Simulated Annealing (Kincaid) -- Part IV Population based Metaheuristics: Scatter Search (Mart?nez-Gavara) -- Memetic Algorithms (Hao) -- Part V Extensions: Data Mining in Heuristic Search (Martins) -- Multi-objective Optimization (Colmenar). 330 $aThis book demonstrates the metaheuristic methodologies that apply to maximum diversity problems to solve them. Maximum diversity problems arise in many practical settings from facility location to social network analysis and constitute an important class of NP-hard problems in combinatorial optimization. In fact, this volume presents a ?missing link? in the combinatorial optimization-related literature. In providing the basic principles and fundamental ideas of the most successful methodologies for discrete optimization, this book allows readers to create their own applications for other discrete optimization problems. Additionally, the book is designed to be useful and accessible to researchers and practitioners in management science, industrial engineering, economics, and computer science, while also extending value to non-experts in combinatorial optimization. Owed to the tutorials presented in each chapter, this book may be used in a master course, a doctoral seminar, or as supplementary to a primary text in upper undergraduate courses. The chapters are divided into three main sections. The first section describes a metaheuristic methodology in a tutorial style, offering generic descriptions that, when applied, create an implementation of the methodology for any optimization problem. The second section presents the customization of the methodology to a given diversity problem, showing how to go from theory to application in creating a heuristic. The final part of the chapters is devoted to experimentation, describing the results obtained with the heuristic when solving the diversity problem. Experiments in the book target the so-called MDPLIB set of instances as a benchmark to evaluate the performance of the methods. 410 0$aSpringer Optimization and Its Applications,$x1931-6836 ;$v204 606 $aMathematical optimization 606 $aCalculus of variations 606 $aAlgorithms 606 $aCalculus of Variations and Optimization 606 $aAlgorithms 606 $aDiscrete Optimization 615 0$aMathematical optimization. 615 0$aCalculus of variations. 615 0$aAlgorithms. 615 14$aCalculus of Variations and Optimization. 615 24$aAlgorithms. 615 24$aDiscrete Optimization. 676 $a519.6 700 $aMartí$b Rafael$0510717 701 $aMartínez-Gavara$b Anna$01449240 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910765496303321 996 $aDiscrete Diversity and Dispersion Maximization$93645567 997 $aUNINA