Multi-objective combinatorial optimization problems and solution methods

Detalles Bibliográficos
Clasificación:Libro Electrónico
Otros Autores: Toloo, Mehdi, Talatahari, Siamak, Rahimi, Iman
Formato: Electrónico eBook
Idioma:Inglés
Publicado: London : Academic Press, 2022.
Temas:
Combinatorial optimization.
Optimisation combinatoire.
Combinatorial optimization
Acceso en línea:Texto completo
Tabla de Contenidos:
  • 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
  • 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
  • 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
  • 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
  • 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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