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|a Multi-objective combinatorial optimization problems and solution methods
|h [electronic resource] /
|c edited by Mehdi Toloo, Siamak Talatahari and Iman Rahimi.
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| 260 |
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|a London :
|b Academic Press,
|c 2022.
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|a 1 online resource
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|a text
|2 rdacontent
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|a computer
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|a online resource
|2 rdacarrier
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|a Front cover -- Half title -- Title -- Copyright -- Dedication -- Contents -- Contributors -- Editors Biography -- Preface -- Acknowledgments -- Chapter 1 Multiobjective combinatorial optimization problems: social, keywords, and journal maps -- 1.1 Introduction -- 1.2 Methodology -- 1.3 Data and basic statistics -- 1.4 Results and discussion -- 1.4.1 Mapping the cognitive space -- 1.4.2 Mapping the social space -- 1.5 Conclusions and direction for future research -- References
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|a Chapter 2 The fundamentals and potential of heuristics and metaheuristics for multiobjective combinatorial optimization problems and solution methods -- 2.1 Introduction -- 2.2 Multiobjective combinatorial optimization -- 2.3 Heuristics concepts -- 2.4 Metaheuristics concepts -- 2.5 Heuristics and metaheuristics examples -- 2.5.1 Tabu search -- 2.6 Evolutionary algorithms (EA) -- 2.7 Genetic algorithms (GA) -- 2.8 Simulated annealing -- 2.9 Particle swarm optimization (PSO) -- 2.10 Scatter search (SS) -- 2.11 Greedy randomized adaptive search procedures (GRASP) -- 2.12 Ant-colony optimization
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|a 2.13 Clustering search -- 2.14 Hybrid metaheuristics -- 2.15 Differential evolution (DE) -- 2.16 Teaching learning-based optimization (TLBO) -- 2.17 Discussion -- 2.18 Conclusions -- 2.19 Future trends -- References -- Chapter 3 A survey on links between multiple objective decision making and data envelopment analysis -- 3.1 Introduction -- 3.2 Preliminary discussion -- 3.2.1 Multiple objective decision making -- 3.2.2 Data envelopment analysis -- 3.3 Application of MODM concepts in the DEA methodology -- 3.3.1 Classical DEA models -- 3.3.2 Target setting -- 3.3.3 Value efficiency
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|a 3.3.4 Secondary goal models -- 3.3.5 Common set of weights -- 3.3.6 DEA-discriminant analysis -- 3.3.7 Efficient units and efficient hyperplanes -- 3.4 Classification of usage of DEA in MODM -- 3.4.1 Efficient points -- 3.5 Discussion and conclusion -- References -- Chapter 4 Improved crow search algorithm based on arithmetic crossover-a novel metaheuristic technique for solving engineering optimization problems -- 4.1 Introduction -- 4.2 Materials and methods -- 4.2.1 Crow search optimization -- 4.2.2 Arithmetic crossover based on genetic algorithm -- 4.2.3 Hybrid CO algorithm
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|a 4.3 Results and discussion -- 4.4 Conclusion -- Acknowledgments -- References -- Chapter 5 MOGROM: Multiobjective Golden Ratio Optimization Algorithm -- 5.1 Introduction -- 5.1.1 Definition of multiobjective problems (MOPs) -- 5.1.2 Literature review -- 5.1.3 Background and related work -- 5.2 GROM and MOGROM -- 5.2.1 MOGROM -- 5.3 Simulation results, investigation, and analysis -- 5.3.1 First class -- 5.3.2 Second class -- 5.3.3 Third class -- 5.3.4 Fourth class -- 5.3.5 Fifth class -- 5.4 Conclusion -- References
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| 650 |
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0 |
|a Combinatorial optimization.
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| 650 |
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6 |
|a Optimisation combinatoire.
|0 (CaQQLa)201-0155776
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| 650 |
|
7 |
|a Combinatorial optimization
|2 fast
|0 (OCoLC)fst00868980
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| 700 |
1 |
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|a Toloo, Mehdi.
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| 700 |
1 |
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|a Talatahari, Siamak.
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| 700 |
1 |
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|a Rahimi, Iman.
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| 776 |
0 |
8 |
|i Print version:
|z 0128237996
|z 9780128237991
|w (OCoLC)1265457617
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| 856 |
4 |
0 |
|u https://sciencedirect.uam.elogim.com/science/book/9780128237991
|z Texto completo
|
